概述
ECCV2018 收录论文整理，共774篇。
百度云链接：https://pan.baidu.com/s/1Mg0Kw9bepUK6_vqqVSOjNQ ，密码：mh97。
以下是下载后的文件：
以下是文件名对应的论文名称：
|
序号 | 文件名 | 论文题目 |
| 1 | Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper.pdf | Improving Shape Deformation inUnsupervised Image-to-Image Translation |
| 2 | Aashish_Sharma_Into_the_Twilight_ECCV_2018_paper.pdf | Into the Twilight Zone: Depth Estimation usingJoint Structure-Stereo Optimization |
| 3 | Aayush_Bansal_Recycle-GAN_Unsupervised_Video_ECCV_2018_paper.pdf | Recycle-GAN: Unsupervised Video Retargeting |
| 4 | Abdullah_Abuolaim_Revisiting_Autofocus_for_ECCV_2018_paper.pdf | Revisiting Autofocus for Smartphone Cameras |
| 5 | Abhimanyu_Dubey_Coreset-Based_Convolutional_Neural_ECCV_2018_paper.pdf | Coreset-Based Neural Network Compression |
| 6 | Abhimanyu_Dubey_Improving_Fine-Grained_Visual_ECCV_2018_paper.pdf | Pairwise Confusionfor Fine-Grained Visual Classification |
| 7 | Adam_Geva_X-ray_Computational_Tomography_ECCV_2018_paper.pdf | X-ray Computed Tomography Through Scatter |
| 8 | Adrian_Bulat_To_learn_image_ECCV_2018_paper.pdf | To learn image super-resolution, use a GAN tolearn how to do image degradation first |
| 9 | Adria_Recasens_Learning_to_Zoom_ECCV_2018_paper.pdf | Learning to Zoom: a Saliency-Based SamplingLayer for Neural Networks |
| 10 | Adrien_Kaiser_Proxy_Clouds_for_ECCV_2018_paper.pdf | Proxy Clouds for Live RGB-D StreamProcessing and Consolidation |
| 11 | Aggeliki_Tsoli_Joint_3D_tracking_ECCV_2018_paper.pdf | Joint 3D Tracking of a Deformable Ob jectin Interaction with a Hand |
| 12 | Ahmet_Iscen_Local_Orthogonal-Group_Testing_ECCV_2018_paper.pdf | Local Orthogonal-Group Testing |
| 13 | Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper.pdf | Improving Sequential Determinantal Point Processes forSupervised Video Summarization |
| 14 | Albert_Pumarola_Anatomically_Coherent_Facial_ECCV_2018_paper.pdf | GANimation: Anatomically-aware FacialAnimation from a Single Image |
| 15 | Alexander_Vakhitov_Stereo_relative_pose_ECCV_2018_paper.pdf | Stereo relative pose from line and point featuretriplets |
| 16 | Alex_Locher_Progressive_Structure_from_ECCV_2018_paper.pdf | Progressive Structure from Motion |
| 17 | Alex_Zhu_Realtime_Time_Synchronized_ECCV_2018_paper.pdf | Realtime Time Synchronized Event-based Stereo |
| 18 | Ali_Diba_Spatio-Temporal_Channel_Correlation_ECCV_2018_paper.pdf | Spatio-Temporal Channel Correlation Networksfor Action Classification |
| 19 | Ameya_Prabhu_Deep_Expander_Networks_ECCV_2018_paper.pdf | Deep Expander Networks:Efficient Deep Networks from Graph Theory |
| 20 | Amir_Mazaheri_Visual_Text_Correction_ECCV_2018_paper.pdf | Visual Text Correction |
| 21 | Amir_Sadeghian_CAR-Net_Clairvoyant_Attentive_ECCV_2018_paper.pdf | CAR-Net: Clairvoyant Attentive Recurrent Network |
| 22 | Amit_Raj_SwapNet_Garment_Transfer_ECCV_2018_paper.pdf | SwapNet: Image Based Garment Transfer |
| 23 | Ananya_Harsh_Jha_Disentangling_Factors_of_ECCV_2018_paper.pdf | Disentangling Factors of Variation withCycle-Consistent Variational Auto-Encoders |
| 24 | Andreas_Veit_Convolutional_Networks_with_ECCV_2018_paper.pdf | Convolutional Networks withAdaptive Inference Graphs |
| 25 | Andrew_Gilbert_Volumetric_performance_capture_ECCV_2018_paper.pdf | Volumetric performance capture from minimalcamera viewpoints |
| 26 | Andrew_Owens_Audio-Visual_Scene_Analysis_ECCV_2018_paper.pdf | Audio-Visual Scene Analysis withSelf-Supervised Multisensory Features |
| 27 | Angela_Dai_3DMV_Joint_3D-Multi-View_ECCV_2018_paper.pdf | 3DMV: Joint 3D-Multi-View Prediction for 3DSemantic Scene Segmentation |
| 28 | Angjoo_Kanazawa_Learning_Category-Specific_Mesh_ECCV_2018_paper.pdf | Learning Category-Specific Mesh Reconstructionfrom Image Collections |
| 29 | Anil_Baslamisli_Joint_Learning_of_ECCV_2018_paper.pdf | Joint Learning of Intrinsic Images and SemanticSegmentation |
| 30 | Anirudh_Som_Perturbation_Robust_Representations_ECCV_2018_paper.pdf | Perturbation Robust Representations ofTopological Persistence Diagrams∗ |
| 31 | Ankan_Bansal_Zero-Shot_Object_Detection_ECCV_2018_paper.pdf | Zero-Shot Object Detection |
| 32 | Antonio_Torralba_Interpretable_Basis_Decomposition_ECCV_2018_paper.pdf | Interpretable Basis Decompositionfor Visual Explanation |
| 33 | Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper.pdf | Weakly- and Semi-Supervised Panoptic Segmentation |
| 34 | Anurag_Ranjan_Generating_3D_Faces_ECCV_2018_paper.pdf | Generating 3D faces using Convolutional MeshAutoencoders |
| 35 | Apoorv_Vyas_Out-of-Distribution_Detection_Using_ECCV_2018_paper.pdf | Out-of-Distribution Detection Using an Ensembleof Self Supervised Leave-out Classifiers |
| 36 | Archan_Ray_U-PC_Unsupervised_Planogram_ECCV_2018_paper.pdf | U-PC: Unsupervised Planogram Compliance |
| 37 | Arjun_Nitin_Bhagoji_Practical_Black-box_Attacks_ECCV_2018_paper.pdf | Practical Black-box Attacks on Deep NeuralNetworks using Efficient Query Mechanisms |
| 38 | Armand_Zampieri_Multimodal_image_alignment_ECCV_2018_paper.pdf | Multimodal image alignmentthrough a multiscale chain of neural networkswith application to remote sensing |
| 39 | Arslan_Chaudhry__Riemannian_Walk_ECCV_2018_paper.pdf | Riemannian Walk for Incremental Learning:Understanding Forgetting and Intransigence |
| 40 | Artsiom_Sanakoyeu_A_Style-aware_Content_ECCV_2018_paper.pdf | A Style-Aware Content Loss forReal-time HD Style Transfer |
| 41 | Arun_Mallya_Piggyback_Adapting_a_ECCV_2018_paper.pdf | Piggyback: Adapting a Single Network toMultiple Tasks by Learning to Mask Weights |
| 42 | Attila_Szabo_Understanding_Degeneracies_and_ECCV_2018_paper.pdf | Understanding Degeneracies and Ambiguitiesin Attribute Transfer |
| 43 | Auston_Sterling_ISNN_-_Impact_ECCV_2018_paper.pdf | ISNN: Impact Sound Neural Network forAudio-Visual Ob ject Classification |
| 44 | Baosheng_Yu_Correcting_the_Triplet_ECCV_2018_paper.pdf | Correcting the Triplet Selection Biasfor Triplet Loss |
| 45 | Baris_Gecer_Semi-supervised_Adversarial_Learning_ECCV_2018_paper.pdf | Semi-supervised Adversarial Learning to GeneratePhotorealistic Face Images of New Identities from 3DMorphable Model |
| 46 | Beery_Recognition_in_Terra_ECCV_2018_paper.pdf | Recognition in Terra Incognita |
| 47 | Benjamin_Coors_SphereNet_Learning_Spherical_ECCV_2018_paper.pdf | SphereNet: Learning Spherical Representations forDetection and Classification in Omnidirectional Images |
| 48 | Benjamin_Eckart_Fast_and_Accurate_ECCV_2018_paper.pdf | HGMR: Hierarchical Gaussian Mixtures forAdaptive 3D Registration |
| 49 | Benjamin_Hepp_Learn-to-Score_Efficient_3D_ECCV_2018_paper.pdf | Learn-to-Score: Efficient 3D Scene Exploration byPredicting View Utility |
| 50 | Bharath_Bhushan_Damodaran_DeepJDOT_Deep_Joint_ECCV_2018_paper.pdf | DeepJDOT: Deep Joint Distribution OptimalTransport for Unsupervised Domain Adaptation |
| 51 | Bingbin_Liu_Temporal_Modular_Networks_ECCV_2018_paper.pdf | Temporal Modular Networks for RetrievingComplex Compositional Activities in Videos |
| 52 | Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.pdf | Simple Baselines for Human Pose Estimationand Tracking |
| 53 | Bochao_Wang_Toward_Characteristic-Preserving_Image-based_ECCV_2018_paper.pdf | Toward Characteristic-Preserving Image-basedVirtual Try-On Network |
| 54 | Bogdan_Bugaev_Combining_3D_Model_ECCV_2018_paper.pdf | Combining 3D Model Contour Energyand Keypoints for Ob ject Tracking |
| 55 | Bolei_Zhou_Temporal_Relational_Reasoning_ECCV_2018_paper.pdf | Temporal Relational Reasoning in Videos |
| 56 | Borui_Jiang_Acquisition_of_Localization_ECCV_2018_paper.pdf | Acquisition of Localization Confidence forAccurate Ob ject Detection |
| 57 | Bowen_Cheng_Revisiting_RCNN_On_ECCV_2018_paper.pdf | Revisiting RCNN: On Awakening theClassification Power of Faster RCNN |
| 58 | Bowen_Zhang_Cross-Modal_and_Hierarchical_ECCV_2018_paper.pdf | Cross-Modal and Hierarchical Modeling ofVideo and Text |
| 59 | Boyu_Chen_Real-time_Actor-Critic_Tracking_ECCV_2018_paper.pdf | Real-time ‘Actor-Critic’ Tracking |
| 60 | Bo_Dai_Rethinking_the_Form_ECCV_2018_paper.pdf | Rethinking the Form of Latent Statesin Image Captioning |
| 61 | Bo_Peng_Extreme_Network_Compression_ECCV_2018_paper.pdf | Extreme Network Compression via Filter GroupApproximation |
| 62 | Bo_Xiong_Snap_Angle_Prediction_ECCV_2018_paper.pdf | Snap Angle Prediction for 360◦ Panoramas |
| 63 | Bo_Zhao_Modular_Generative_Adversarial_ECCV_2018_paper.pdf | Modular Generative Adversarial Networks |
| 64 | Brandon_RichardWebster_Visual_Psychophysics_for_ECCV_2018_paper.pdf | Visual Psychophysics for Making FaceRecognition Algorithms More Explainable |
| 65 | Brook_Roberts_A_Dataset_for_ECCV_2018_paper.pdf | A Dataset for Lane Instance Segmentation inUrban Environments |
| 66 | Bruce_Hou_Transferable_Adversarial_Perturbations_ECCV_2018_paper.pdf | Transferable Adversarial Perturbations |
| 67 | Bryan_Plummer_Conditional_Image-Text_Embedding_ECCV_2018_paper.pdf | Conditional Image-Text Embedding Networks |
| 68 | Calvin_Murdock_Deep_Component_Analysis_ECCV_2018_paper.pdf | Deep Component Analysis viaAlternating Direction Neural Networks |
| 69 | Carlos_Esteves_Learning_SO3_Equivariant_ECCV_2018_paper.pdf | Learning SO(3) Equivariant Representationswith Spherical CNNs |
| 70 | Carl_Toft_Semantic_Match_Consistency_ECCV_2018_paper.pdf | Semantic Match Consistency for Long-TermVisual Localization |
| 71 | Carl_Vondrick_Self-supervised_Tracking_by_ECCV_2018_paper.pdf | Tracking Emerges by Colorizing Videos |
| 72 | Ceyuan_Yang_Pose_Guided_Human_ECCV_2018_paper.pdf | Pose Guided Human Video Generation |
| 73 | Changan_Chen_Constraints_Matter_in_ECCV_2018_paper.pdf | Constraint-Aware Deep Neural NetworkCompression |
| 74 | Changqian_Yu_BiSeNet_Bilateral_Segmentation_ECCV_2018_paper.pdf | BiSeNet: Bilateral Segmentation Network forReal-time Semantic Segmentation |
| 75 | Changqing_Zou_SketchyScene_Richly-Annotated_Scene_ECCV_2018_paper.pdf | SketchyScene: Richly-Annotated Scene Sketches |
| 76 | Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.pdf | Deep Boosting for Image Denoising |
| 77 | Chang_Liu_Linear_Span_Network_ECCV_2018_paper.pdf | Linear Span Network for Ob ject SkeletonDetection |
| 78 | Chanho_Kim_Multi-object_Tracking_with_ECCV_2018_paper.pdf | Multi-ob ject Tracking with Neural Gating UsingBilinear LSTM |
| 79 | Chao-Yuan_Wu_Video_Compression_through_ECCV_2018_paper.pdf | Video Compression through Image Interpolation |
| 80 | Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.pdf | Hierarchical Bilinear Poolingfor Fine-Grained Visual Recognition |
| 81 | CHAOWEI_XIAO_Characterize_Adversarial_Examples_ECCV_2018_paper.pdf | Characterizing Adversarial Examples Based onSpatial Consistency Information for SemanticSegmentation |
| 82 | Chao_Li_ArticulatedFusion_Real-time_Reconstruction_ECCV_2018_paper.pdf | ArticulatedFusion: Real-time Reconstruction ofMotion, Geometry and Segmentation Using aSingle Depth Camera |
| 83 | Chao_Wang_Discriminative_Region_Proposal_ECCV_2018_paper.pdf | Discriminative Region Proposal AdversarialNetworks for High-Quality Image-to-ImageTranslation |
| 84 | Charles_Herrmann_Object-centered_image_stitching_ECCV_2018_paper.pdf | Object-centered image stitching |
| 85 | Charles_Herrmann_Robust_image_stitching_ECCV_2018_paper.pdf | Robust image stitching with multiple registrations |
| 86 | Chenglong_Li_Cross-Modal_Ranking_with_ECCV_2018_paper.pdf | Cross-Modal Ranking with Soft Consistency andNoisy Labels for Robust RGB-T Tracking |
| 87 | Cheng_Wang_Mancs_A_Multi-task_ECCV_2018_paper.pdf | Mancs: A Multi-task Attentional Network withCurriculum Sampling for PersonRe-identification |
| 88 | Chenxi_Liu_Progressive_Neural_Architecture_ECCV_2018_paper.pdf | Progressive Neural Architecture Search |
| 89 | Chenyang_Si_Skeleton-Based_Action_Recognition_ECCV_2018_paper.pdf | Skeleton-Based Action Recognition with SpatialReasoning and Temporal Stack Learning |
| 90 | Chen_Liu_FloorNet_A_Unified_ECCV_2018_paper.pdf | FloorNet: A Unified Framework for FloorplanReconstruction from 3D Scans |
| 91 | Chen_Sun_Actor-centric_Relation_Network_ECCV_2018_paper.pdf | Actor-Centric Relation Network |
| 92 | Chen_Zhu_Fine-grained_Video_Categorization_ECCV_2018_paper.pdf | Fine-grained Video Categorization withRedundancy Reduction Attention |
| 93 | Chieh_Lin_Escaping_from_Collapsing_ECCV_2018_paper.pdf | Escaping from Collapsing Modes in aConstrained Space |
| 94 | Chi_Li_A_Unified_Framework_ECCV_2018_paper.pdf | A Unified Framework for Multi-View Multi-ClassObject Pose Estimation |
| 95 | Chong_Li_Constrained_Optimization_Based_ECCV_2018_paper.pdf | Constrained Optimization Based Low-RankApproximation of Deep Neural Networks |
| 96 | Chong_You_A_Scalable_Exemplar-based_ECCV_2018_paper.pdf | A Scalable Exemplar-based Subspace ClusteringAlgorithm for Class-Imbalanced Data |
| 97 | Christopher_Zach_Descending_lifting_or_ECCV_2018_paper.pdf | Descending, lifting or smoothing:Secrets of robust cost optimization |
| 98 | Christos_Sakaridis_Semantic_Scene_Understanding_ECCV_2018_paper.pdf | Model Adaptation with Synthetic and Real Datafor Semantic Dense Foggy Scene Understanding |
| 99 | Chuanxia_Zheng_T2Net_Synthetic-to-Realistic_Translation_ECCV_2018_paper.pdf | T2Net: Synthetic-to-Realistic Translation forSolving Single-Image Depth Estimation Tasks |
| 100 | Chuhui_Xue_Accurate_Scene_Text_ECCV_2018_paper.pdf | Accurate Scene Text Detection through BorderSemantics Awareness and Bootstrapping |
| 101 | CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf | Bi-box Regression for Pedestrian Detection andOcclusion Estimation |
| 102 | Chunrui_Han_Face_Recognition_with_ECCV_2018_paper.pdf | Face Recognition with Contrastive Convolution |
| 103 | Chunyan_Bai_Deep_Video_Generation_ECCV_2018_paper.pdf | Deep Video Generation, Prediction andCompletion of Human Action Sequences |
| 104 | Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.pdf | Graininess-Aware Deep Feature Learning forPedestrian Detection |
| 105 | Chu_Wang_Local_Spectral_Graph_ECCV_2018_paper.pdf | Local Spectral Graph Convolution for Point Set FeatureLearning |
| 106 | Ciprian_Corneanu_Deep_Structure_Inference_ECCV_2018_paper.pdf | Deep Structure Inference Network for FacialAction Unit Recognition |
| 107 | Clement_Godard_Deep_Burst_Denoising_ECCV_2018_paper.pdf | Deep Burst Denoising |
| 108 | Csaba_Domokos_MRF_Optimization_with_ECCV_2018_paper.pdf | MRF Optimization with Separable Convex Prioron Partially Ordered Labels |
| 109 | Curtis_Wigington_Start_Follow_Read_ECCV_2018_paper.pdf | Start, Follow, Read: End-to-End Full-PageHandwriting Recognition |
| 110 | Damien_Teney_Visual_Question_Answering_ECCV_2018_paper.pdf | Visual Question Answering as aMeta Learning Task |
| 111 | Danda_Pani_Paudel_Sampling_Algebraic_Varieties_ECCV_2018_paper.pdf | Sampling Algebraic Varieties for Robust CameraAutocalibration |
| 112 | Danfeng_Hong_Joint__Progressive_ECCV_2018_paper.pdf | Joint & Progressive Learning fromHigh-Dimensional Data for Multi-LabelClassification |
| 113 | Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf | Multi-Class Model Fitting by Energy Minimization andMode-Seeking |
| 114 | Daniel_Castro_From_Face_Recognition_ECCV_2018_paper.pdf | From Face Recognition to Models of Identity:A Bayesian Approach to Learning aboutUnknown Identities from Unsupervised Data |
| 115 | Daniel_Gehrig_Asynchronous_Photometric_Feature_ECCV_2018_paper.pdf | Asynchronous, Photometric Feature Trackingusing Events and Frames |
| 116 | Daniel_Jakubovitz_Improving_DNN_Robustness_ECCV_2018_paper.pdf | Improving DNN Robustness to AdversarialAttacks using Jacobian Regularization |
| 117 | Daniel_Maurer_Structure-from-Motion-Aware_PatchMatch_for_ECCV_2018_paper.pdf | Structure-from-Motion-Aware PatchMatchfor Adaptive Optical Flow Estimation |
| 118 | Daniel_Worrall_CubeNet_Equivariance_to_ECCV_2018_paper.pdf | CubeNet: Equivariance to 3D Rotationand Translation |
| 119 | Dapeng_Chen_Improving_Deep_Visual_ECCV_2018_paper.pdf | Improving Deep Visual Representation forPerson Re-identification by Global and LocalImage-language Association |
| 120 | Dario_Rethage_Fully-Convolutional_Point_Networks_ECCV_2018_paper.pdf | Fully-Convolutional Point Networksfor Large-Scale Point Clouds |
| 121 | David_Harwath_Jointly_Discovering_Visual_ECCV_2018_paper.pdf | Jointly Discovering Visual Objects and Spoken Wordsfrom Raw Sensory Input |
| 122 | David_Schubert_Direct_Sparse_Odometry_ECCV_2018_paper.pdf | Direct Sparse Odometry with Rolling Shutter |
| 123 | Dawei_Du_The_Unmanned_Aerial_ECCV_2018_paper.pdf | The Unmanned Aerial Vehicle Benchmark:Ob ject Detection and Tracking |
| 124 | Deng-Ping_Fan_Salient_Objects_in_ECCV_2018_paper.pdf | Salient Ob jects in Clutter: Bringing SalientOb ject Detection to the Foreground |
| 125 | Dhruv_Mahajan_Exploring_the_Limits_ECCV_2018_paper.pdf | Exploring the Limits ofWeakly Supervised Pretraining |
| 126 | Diana_Sungatullina_Image_Manipulation_with_ECCV_2018_paper.pdf | Image Manipulation withPerceptual Discriminators |
| 127 | Dian_SHAO_Find_and_Focus_ECCV_2018_paper.pdf | Find and Focus: Retrieve and LocalizeVideo Events with Natural Language Queries |
| 128 | Dima_Damen_Scaling_Egocentric_Vision_ECCV_2018_paper.pdf | Scaling Egocentric Vision:The EPIC-KITCHENS Dataset |
| 129 | Dinesh_Jayaraman_ShapeCodes_Self-Supervised_Feature_ECCV_2018_paper.pdf | ShapeCodes: Self-Supervised Feature Learningby Lifting Views to Viewgrids |
| 130 | Diwen_Wan_TBN_Convolutional_Neural_ECCV_2018_paper.pdf | TBN: Convolutional Neural Network withTernary Inputs and Binary Weights |
| 131 | Di_Chen_Person_Search_via_ECCV_2018_paper.pdf | Person Search via A Mask-Guided Two-Stream CNNModel |
| 132 | Di_Lin_Multi-Scale_Context_Intertwining_ECCV_2018_paper.pdf | Multi-Scale Context Intertwiningfor Semantic Segmentation |
| 133 | Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper.pdf | Revisiting the Inverted Indices for Billion-ScaleApproximate Nearest Neighbors |
| 134 | Dmytro_Mishkin_Repeatability_Is_Not_ECCV_2018_paper.pdf | Repeatability Is Not Enough:Learning Affine Regions via Discriminability |
| 135 | Dongang_Wang_Dividing_and_Aggregating_ECCV_2018_paper.pdf | Dividing and Aggregating Network forMulti-view Action Recognition |
| 136 | Donghoon_Lee_Unsupervised_holistic_image_ECCV_2018_paper.pdf | Unsupervised Holistic Image Generation fromKey Local Patches |
| 137 | Dongqing_Zhang_Optimized_Quantization_for_ECCV_2018_paper.pdf | LQ-Nets: Learned Quantization for HighlyAccurate and Compact Deep Neural Networks |
| 138 | Dongwoo_Lee_Joint_Blind_Motion_ECCV_2018_paper.pdf | Joint Blind Motion Deblurring and DepthEstimation of Light Field |
| 139 | Dong_Lao_Extending_Layered_Models_ECCV_2018_paper.pdf | Extending Layered Models to 3D Motion |
| 140 | Dong_Li_Recurrent_Tubelet_Proposal_ECCV_2018_paper.pdf | Recurrent Tubelet Proposal and RecognitionNetworks for Action Detection |
| 141 | Dong_Su_Is_Robustness_the_ECCV_2018_paper.pdf | Is Robustness the Cost of Accuracy?– A Comprehensive Study on the Robustness of18 Deep Image Classification Models |
| 142 | Dong_Yang_Proximal_Dehaze-Net_A_ECCV_2018_paper.pdf | Proximal Dehaze-Net: A Prior Learning-Based DeepNetwork for Single Image Dehazing |
| 143 | Eddy_Ilg_Occlusions_Motion_and_ECCV_2018_paper.pdf | Occlusions, Motion and Depth Boundaries witha Generic Network for Disparity, Optical Flowor Scene Flow Estimation |
| 144 | Eddy_Ilg_Uncertainty_Estimates_and_ECCV_2018_paper.pdf | Uncertainty Estimates and Multi-HypothesesNetworks for Optical Flow |
| 145 | Edgar_Margffoy-Tuay_Dynamic_Multimodal_Instance_ECCV_2018_paper.pdf | Dynamic Multimodal Instance SegmentationGuided by Natural Language Queries |
| 146 | Edouard_Oyallon_Compressing_the_Input_ECCV_2018_paper.pdf | Compressing the Input for CNNs with theFirst-Order Scattering Transform |
| 147 | Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.pdf | Deep Feature Factorization For ConceptDiscovery |
| 148 | Efstratios_Gavves_Long-term_Tracking_in_ECCV_2018_paper.pdf | Long-term Tracking in the Wild: A Benchmark |
| 149 | Eric_Muller-Budack_Geolocation_Estimation_of_ECCV_2018_paper.pdf | Geolocation Estimation of Photos using aHierarchical Model and Scene Classification |
| 150 | Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper.pdf | Multiple-gaze geometry: Inferring novel 3Dlocations from gazes observed in monocularvideo |
| 151 | Eunbyung_Park_Meta-Tracker_Fast_and_ECCV_2018_paper.pdf | Meta-Tracker: Fast and Robust OnlineAdaptation for Visual Ob ject Trackers |
| 152 | Eunhyeok_Park_Value-aware_Quantization_for_ECCV_2018_paper.pdf | Value-aware Quantizationfor Training and Inference of Neural Networks |
| 153 | Eunji_Chong_Connecting_Gaze_Scene_ECCV_2018_paper.pdf | Connecting Gaze, Scene, and Attention:Generalized Attention Estimation via JointModeling of Gaze and Scene Saliency |
| 154 | Fabian_Caba_What_do_I_ECCV_2018_paper.pdf | What do I Annotate Next? An Empirical Studyof Active Learning for Action Localization |
| 155 | Fabian_Manhardt_Deep_Model-Based_6D_ECCV_2018_paper.pdf | Deep Model-Based 6D Pose Refinement in RGB |
| 156 | Fabien_Baradel_Object_Level_Visual_ECCV_2018_paper.pdf | Ob ject Level Visual Reasoning in Videos |
| 157 | Fabio_Tosi_Beyond_local_reasoning_ECCV_2018_paper.pdf | Beyond local reasoning for stereo confidenceestimation with deep learning |
| 158 | Fangneng_Zhan_Verisimilar_Image_Synthesis_ECCV_2018_paper.pdf | Verisimilar Image Synthesis for AccurateDetection and Recognition of Texts in Scenes |
| 159 | Fang_Zhao_Dynamic_Conditional_Networks_ECCV_2018_paper.pdf | Dynamic Conditional Networksfor Few-Shot Learning |
| 160 | Fanyi_Xiao_Object_Detection_with_ECCV_2018_paper.pdf | Video Ob ject Detection with an AlignedSpatial-Temporal Memory |
| 161 | Fatemeh_Sadat_Saleh_Effective_Use_of_ECCV_2018_paper.pdf | Effective Use of Synthetic Data forUrban Scene Semantic Segmentation⋆ |
| 162 | Fatih_Cakir_Hashing_with_Binary_ECCV_2018_paper.pdf | Hashing with Binary Matrix Pursuit |
| 163 | Felipe_Codevilla_On_Offline_Evaluation_ECCV_2018_paper.pdf | On Offline Evaluation of Vision-based Driving Models |
| 164 | Fengting_Yang_Recovering_3D_Planes_ECCV_2018_paper.pdf | Recovering 3D Planes from a Single Image viaConvolutional Neural Networks |
| 165 | Filippos_Kokkinos_Deep_Image_Demosaicking_ECCV_2018_paper.pdf | Deep Image Demosaicking using a Cascade ofConvolutional Residual Denoising Networks |
| 166 | Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper.pdf | Deep Shape Matching |
| 167 | Fitsum_Reda_SDC-Net_Video_prediction_ECCV_2018_paper.pdf | SDC-Net: Video prediction usingspatially-displaced convolution |
| 168 | Florian_Strub_Visual_Reasoning_with_ECCV_2018_paper.pdf | Visual Reasoning with Multi-hop FeatureModulation |
| 169 | Francisco_M._Castro_End-to-End_Incremental_Learning_ECCV_2018_paper.pdf | End-to-End Incremental Learning |
| 170 | Fudong_Wang_Adaptively_Transforming_Graph_ECCV_2018_paper.pdf | Adaptively Transforming Graph Matching |
| 171 | Gang_Zhang_Generative_Adversarial_Network_ECCV_2018_paper.pdf | Generative Adversarial Network with SpatialAttention for Face Attribute Editing |
| 172 | Gaofeng_Meng_Exploiting_Vector_Fields_ECCV_2018_paper.pdf | Exploiting Vector Fields for GeometricRectification of Distorted Document Images |
| 173 | gao_peng_Question-Guided_Hybrid_Convolution_ECCV_2018_paper.pdf | Question-Guided Hybrid Convolution for VisualQuestion Answering |
| 174 | Gedas_Bertasius_Object_Detection_in_ECCV_2018_paper.pdf | Ob ject Detection in Video withSpatiotemporal Sampling Networks |
| 175 | George_Papandreou_PersonLab_Person_Pose_ECCV_2018_paper.pdf | PersonLab: Person Pose Estimation andInstance Segmentation with a Bottom-Up,Part-Based, Geometric Embedding Model |
| 176 | Ge_Deep_Metric_Learning_ECCV_2018_paper.pdf | Deep Metric Learning with HierarchicalTriplet Loss |
| 177 | Gilad_Divon_Viewpoint_Estimation_-_ECCV_2018_paper.pdf | Viewpoint Estimation—Insights & Model |
| 178 | Gilles_Simon_A_Contrario_Horizon-First_ECCV_2018_paper.pdf | A-Contrario Horizon-First Vanishing PointDetection Using Second-Order Grouping Laws |
| 179 | Goutam_Bhat_Unveiling_the_Power_ECCV_2018_paper.pdf | Unveiling the Power of Deep Tracking |
| 180 | Gratianus_Wesley_Putra_Data_Interpolating_Convolutional_Neural_ECCV_2018_paper.pdf | Interpolating Convolutional Neural NetworksUsing Batch Normalization |
| 181 | Gregoire_Payen_de_La_Garanderie_Eliminating_the_Dreaded_ECCV_2018_paper.pdf | Eliminating the Blind Spot: Adapting 3D Ob jectDetection and Monocular Depth Estimation to360◦ Panoramic Imagery |
| 182 | Guanan_Wang_Semi-Supervised_Generative_Adversarial_ECCV_2018_paper.pdf | Semi-Supervised Generative Adversarial Hashingfor Image Retrieval |
| 183 | Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.pdf | Learning Single-View 3D Reconstruction withLimited Pose Supervision |
| 184 | Guangming_Zang_Super-Resolution_and_Sparse_ECCV_2018_paper.pdf | Super-Resolution and Sparse ViewCT Reconstruction |
| 185 | Guangyu_Robert_Yang_A_dataset_and_ECCV_2018_paper.pdf | A Dataset and Architecture for VisualReasoning with a Working Memory |
| 186 | Guanying_Chen_PS-FCN_A_Flexible_ECCV_2018_paper.pdf | PS-FCN: A Flexible Learning Framework forPhotometric Stereo |
| 187 | Guilin_Liu_Image_Inpainting_for_ECCV_2018_paper.pdf | Image Inpainting for Irregular Holes UsingPartial Convolutions |
| 188 | Gul_Varol_BodyNet_Volumetric_Inference_ECCV_2018_paper.pdf | BodyNet: Volumetric Inference of3D Human Body Shapes |
| 189 | Guojun_Yin_Zoom-Net_Mining_Deep_ECCV_2018_paper.pdf | Zoom-Net: Mining Deep Feature Interactions forVisual Relationship Recognition |
| 190 | Guoliang_Kang_Deep_Adversarial_Attention_ECCV_2018_paper.pdf | Deep Adversarial Attention Alignment forUnsupervised Domain Adaptation:the Benefit of Target Expectation Maximization |
| 191 | Guorun_Yang_SegStereo_Exploiting_Semantic_ECCV_2018_paper.pdf | SegStereo: Exploiting Semantic Information forDisparity Estimation |
| 192 | Guosheng_Hu_Deep_Multi-Task_Learning_ECCV_2018_paper.pdf | Deep Multi-Task Learning to Recognise SubtleFacial Expressions of Mental States |
| 193 | Guo_Lu_Deep_Kalman_Filtering_ECCV_2018_paper.pdf | Image Reassembly Combining Deep Learningand Shortest Path Problem |
| 194 | Haitian_Zheng_CrossNet_An_End-to-end_ECCV_2018_paper.pdf | CrossNet: An End-to-end Reference-based SuperResolution Network using Cross-scale Warping |
| 195 | Hai_Ci_Video_Object_Segmentation_ECCV_2018_paper.pdf | Video Ob ject Segmentation by LearningLocation-Sensitive Embeddings |
| 196 | Hang_Yan_RIDI_Robust_IMU_ECCV_2018_paper.pdf | RIDI: Robust IMU Double Integration |
| 197 | Hang_Zhao_The_Sound_of_ECCV_2018_paper.pdf | The Sound of Pixels |
| 198 | Hanyu_Wang_Learning_3D_Keypoint_ECCV_2018_paper.pdf | Learning 3D Keypoint Descriptors forNon-Rigid Shape Matching |
| 199 | Haoshuo_Huang_Domain_transfer_through_ECCV_2018_paper.pdf | Domain transfer through deep activationmatching |
| 200 | Haoshu_Fang_Pairwise_Body-Part_Attention_ECCV_2018_paper.pdf | Pairwise Body-Part Attention for RecognizingHuman-Ob ject Interactions |
| 201 | Hao_Cheng_Evaluating_Capability_of_ECCV_2018_paper.pdf | Evaluating Capability of Deep Neural Networksfor Image Classification via Information Plane |
| 202 | Haroon_Idrees_Composition_Loss_for_ECCV_2018_paper.pdf | Composition Loss for Counting, Density MapEstimation and Localization in Dense Crowds |
| 203 | Heewon_Kim_Task-Aware_Image_Downscaling_ECCV_2018_paper.pdf | Task-Aware Image Downscaling |
| 204 | Hei_Law_CornerNet_Detecting_Objects_ECCV_2018_paper.pdf | CornerNet: Detecting Ob jects asPaired Keypoints |
| 205 | Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.pdf | Unsupervised Geometry-Aware Representationfor 3D Human Pose Estimation |
| 206 | Hengcan_Shi_Key-Word-Aware_Network_for_ECCV_2018_paper.pdf | Key-Word-Aware Network for ReferringExpression Image Segmentation |
| 207 | Hengshuang_Zhao_Compositing-aware_Image_Search_ECCV_2018_paper.pdf | Compositing-aware Image Search |
| 208 | Hengshuang_Zhao_ICNet_for_Real-Time_ECCV_2018_paper.pdf | ICNet for Real-Time Semantic Segmentationon High-Resolution Images |
| 209 | Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf | PSANet: Point-wise Spatial AttentionNetwork for Scene Parsing |
| 210 | Heng_Wang_Scenes-Objects-Actions_A_Multi-Task_ECCV_2018_paper.pdf | Scenes-Ob jects-Actions: A Multi-Task,Multi-Label Video Dataset |
| 211 | Henry_W._F._Yeung_Fast_Light_Field_ECCV_2018_paper.pdf | Fast Light Field Reconstruction With DeepCoarse-To-Fine Modeling of Spatial-Angular Clues |
| 212 | Hieu_Le_AD_Net_Training_ECCV_2018_paper.pdf | A+D Net: Training a Shadow Detector withAdversarial Shadow Attenuation |
| 213 | Hiroaki_Santo_Light_Structure_from_ECCV_2018_paper.pdf | Light Structure from Pin Motion:Simple and Accurate Point LightCalibration for Physics-based Modeling |
| 214 | Hoang_Le_Interactive_Boundary_Prediction_ECCV_2018_paper.pdf | Interactive Boundary Prediction for Ob jectSelection |
| 215 | Hong-Min_Chu_Deep_Generative_Models_ECCV_2018_paper.pdf | Deep Generative Models for Weakly-SupervisedMulti-Label Classification |
| 216 | Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.pdf | Pyramid Dilated Deeper ConvLSTM forVideo Salient Ob ject Detection |
| 217 | Hongyang_Li_Neural_Network_Encapsulation_ECCV_2018_paper.pdf | Neural Network Encapsulation |
| 218 | Hongyu_Xu_Deep_Regionlets_for_ECCV_2018_paper.pdf | Deep Regionlets for Ob ject Detection |
| 219 | Hong_Xuan_Randomized_Ensemble_Embeddings_ECCV_2018_paper.pdf | Deep Randomized Ensembles for MetricLearning |
| 220 | Hossam_Isack_K-convexity_shape_priors_ECCV_2018_paper.pdf | K-convexity shape priors for segmentation |
| 221 | Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.pdf | Diverse Image-to-Image Translation viaDisentangled Representations |
| 222 | HSUAN-I_HO_Summarizing_First-Person_Videos_ECCV_2018_paper.pdf | Summarizing First-Person Videosfrom Third Persons’ Points of Views |
| 223 | Hsueh-Fu_Lu_Toward_Scale-Invariance_and_ECCV_2018_paper.pdf | Toward Scale-Invariance and Position-SensitiveRegion Proposal Networks |
| 224 | Huaizu_Jiang_Self-Supervised_Relative_Depth_ECCV_2018_paper.pdf | Self-Supervised Relative Depth Learning for UrbanScene Understanding |
| 225 | Huajie_Jiang_Learning_Class_Prototypes_ECCV_2018_paper.pdf | Learning Class Prototypes via StructureAlignment for Zero-Shot Recognition |
| 226 | Huang_Predicting_Gaze_in_ECCV_2018_paper.pdf | Predicting Gaze in Egocentric Video byLearning Task-dependent Attention Transition |
| 227 | Huayi_Zeng_Neural_Procedural_Reconstruction_ECCV_2018_paper.pdf | Neural Procedural Reconstruction forResidential Buildings |
| 228 | Huizhong_Zhou_DeepTAM_Deep_Tracking_ECCV_2018_paper.pdf | DeepTAM: Deep Tracking and Mapping |
| 229 | Humam_Alwassel_Action_Search_Spotting_ECCV_2018_paper.pdf | Action Search: Spotting Actions in Videos andIts Application to Temporal Action Localization |
| 230 | Humam_Alwassel_Diagnosing_Error_in_ECCV_2018_paper.pdf | Diagnosing Error in Temporal Action Detectors |
| 231 | HUSEYIN_COSKUN_Human_Motion_Analysis_ECCV_2018_paper.pdf | Human Motion Analysis with Deep Metric Learning |
| 232 | HU_Jian-Fang_Deep_Bilinear_Learning_ECCV_2018_paper.pdf | Deep Bilinear Learning for RGB-D ActionRecognition |
| 233 | Hyo_Jin_Kim_Hierarchy_of_Alternating_ECCV_2018_paper.pdf | Hierarchy of Alternating Specialistsfor Scene Recognition |
| 234 | Ian_Cherabier_Learning_Priors_for_ECCV_2018_paper.pdf | Learning Priors for Semantic 3D Reconstruction |
| 235 | Ikehata_CNN-PS_CNN-based_Photometric_ECCV_2018_paper.pdf | CNN-PS: CNN-based Photometric Stereo forGeneral Non-Convex Surfaces |
| 236 | Ilchae_Jung_Real-Time_MDNet_ECCV_2018_paper.pdf | Real-Time MDNet |
| 237 | Isma_Hadji_A_New_Large_ECCV_2018_paper.pdf | A New Large Scale Dynamic Texture Datasetwith Application to ConvNet Understanding |
| 238 | Issam_Hadj_Laradji_Where_are_the_ECCV_2018_paper.pdf | Where are the Blobs:Counting by Localization with Point Supervision |
| 239 | Ivan_Eichhardt_Affine_Correspondences_between_ECCV_2018_paper.pdf | Affine Correspondences between CentralCameras for Rapid Relative Pose Estimation |
| 240 | Jacob_Huh_Fighting_Fake_News_ECCV_2018_paper.pdf | Fighting Fake News: Image Splice Detectionvia Learned Self-Consistency |
| 241 | Jiabei_Zeng_Facial_Expression_Recognition_ECCV_2018_paper.pdf | Facial Expression Recognition withInconsistently Annotated Datasets |
| 242 | Jiafan_Zhuang_Towards_Human-Level_License_ECCV_2018_paper.pdf | Towards Human-Level License Plate Recognition |
| 243 | Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.pdf | Efficient Semantic Scene Completion Networkwith Spatial Group Convolution |
| 244 | Jiajun_Wu_Learning_3D_Shape_ECCV_2018_paper.pdf | Learning Shape Priors forSingle-View 3D Completion and Reconstruction |
| 245 | Jialin_Wu_Dynamic_Sampling_Convolutional_ECCV_2018_paper.pdf | Dynamic Filtering with Large Sampling Field forConvNets |
| 246 | Jianbo_Jiao_Look_Deeper_into_ECCV_2018_paper.pdf | Look Deeper into Depth: Monocular DepthEstimation with Semantic Booster andAttention-Driven Loss |
| 247 | Jiangxin_Dong_Learning_Data_Terms_ECCV_2018_paper.pdf | Learning Data Terms for Non-blind Deblurring |
| 248 | Jianwei_Yang_Graph_R-CNN_for_ECCV_2018_paper.pdf | Graph R-CNN for Scene Graph Generation |
| 249 | Jian_Wang_Programmable_Light_Curtains_ECCV_2018_paper.pdf | Programmable Triangulation Light Curtains |
| 250 | Jiawei_He_Probabilistic_Video_Generation_ECCV_2018_paper.pdf | Probabilistic Video Generation usingHolistic Attribute Control |
| 251 | Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.pdf | Deep Cross-modality Adaptation via SemanticsPreserving Adversarial Learning forSketch-based 3D Shape Retrieval |
| 252 | Jiayuan_Gu_Learning_Region_Features_ECCV_2018_paper.pdf | Learning Region Features for Ob ject Detection |
| 253 | Jieru_Mei_Online_Dictionary_Learning_ECCV_2018_paper.pdf | Online Dictionary Learning for ApproximateArchetypal Analysis |
| 254 | Jie_Guo_Single_Image_Highlight_ECCV_2018_paper.pdf | Single Image Highlight Removal with a Sparseand Low-Rank Reflection Model |
| 255 | Jie_Liang_Sub-GAN_An_Unsupervised_ECCV_2018_paper.pdf | Sub-GAN: An Unsupervised Generative Modelvia Subspaces |
| 256 | Jie_Song_Selective_Zero-Shot_Classification_ECCV_2018_paper.pdf | Selective Zero-Shot Classification withAugmented Attributes |
| 257 | Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.pdf | Seeing Deeply and Bidirectionally: A Deep LearningApproach for Single Image Reflection Removal |
| 258 | Jie_Zhang_Geometric_Constrained_Joint_ECCV_2018_paper.pdf | Geometric Constrained Joint Lane Segmentationand Lane Boundary Detection |
| 259 | Jin-Dong_Dong_DPP-Net_Device-aware_Progressive_ECCV_2018_paper.pdf | DPP-Net: Device-aware Progressive Search forPareto-optimal Neural Architectures |
| 260 | Jin-Seok_Park_Double_JPEG_Detection_ECCV_2018_paper.pdf | Double JPEG Detection in Mixed JPEG QualityFactors using Deep Convolutional NeuralNetwork |
| 261 | Jingwei_Ji_End-to-End_Joint_Semantic_ECCV_2018_paper.pdf | End-to-End Joint Semantic Segmentation ofActors and Actions in Video |
| 262 | Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper.pdf | Generative Domain-Migration Hashing forSketch-to-Image Retrieval |
| 263 | Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper.pdf | Textual Explanations for Self-Driving Vehicles |
| 264 | Jinlong_YANG_Analyzing_Clothing_Layer_ECCV_2018_paper.pdf | Analyzing Clothing Layer Deformation Statisticsof 3D Human Motions |
| 265 | Jiqing_Wu_Wasserstein_Divergence_For_ECCV_2018_paper.pdf | Wasserstein Divergence for GANs |
| 266 | Jiren_Zhu_HiDDeN_Hiding_Data_ECCV_2018_paper.pdf | HiDDeN: Hiding Data With Deep Networks |
| 267 | Jiuxiang_Gu_Unpaired_Image_Captioning_ECCV_2018_paper.pdf | Unpaired Image Captioning by LanguagePivoting |
| 268 | Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.pdf | CTAP: Complementary Temporal ActionProposal Generation |
| 269 | Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.pdf | Selfie Video Stabilization |
| 270 | Ji_Zhu_Online_Multi-Object_Tracking_ECCV_2018_paper.pdf | Online Multi-Ob ject Tracking withDual Matching Attention Networks |
| 271 | Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper.pdf | Unsupervised Learning ofMulti-Frame Optical Flow with Occlusions |
| 272 | Johannes_Schoenberger_Learning_to_Fuse_ECCV_2018_paper.pdf | Learning to Fuse Proposals from MultipleScanline Optimizations in Semi-Global Matching |
| 273 | Jongbin_Ryu_DFT-based_Transformation_Invariant_ECCV_2018_paper.pdf | DFT-based Transformation Invariant PoolingLayer for Visual Classification |
| 274 | Joseph_DeGol_Improved_Structure_from_ECCV_2018_paper.pdf | Improved Structure from Motion UsingFiducial Marker Matching |
| 275 | Jue_Wang_Learning_Discriminative_Video_ECCV_2018_paper.pdf | Learning Discriminative Video RepresentationsUsing Adversarial Perturbations |
| 276 | Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.pdf | LSQ++: Lower running time and higher recallin multi-codebook quantization |
| 277 | Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.pdf | Multi-scale Residual Network for ImageSuper-Resolution |
| 278 | Junho_Jeon_Reconstruction-based_Pairwise_Depth_ECCV_2018_paper.pdf | Reconstruction-based Pairwise Depth Dataset forDepth Image Enhancement Using CNN |
| 279 | Junjie_Zhang_Goal-Oriented_Visual_Question_ECCV_2018_paper.pdf | Goal-Oriented Visual Question Generation viaIntermediate Rewards |
| 280 | Junwu_Weng_Deformable_Pose_Traversal_ECCV_2018_paper.pdf | Deformable Pose Traversal Convolutionfor 3D Action and Gesture Recognition |
| 281 | Justin_Liang_End-to-End_Deep_Structured_ECCV_2018_paper.pdf | End-to-End Deep Structured Models forDrawing Crosswalks |
| 282 | Jyh-Jing_Hwang_Adaptive_Affinity_Field_ECCV_2018_paper.pdf | Adaptive Affinity Fields for Semantic Segmentation |
| 283 | Kaicheng_Yu_Statistically-motivated_Second-order_Pooling_ECCV_2018_paper.pdf | Statistically-motivated Second-order Pooling⋆ |
| 284 | Kaipeng_Zhang_Super-Identity_Convolutional_Neural_ECCV_2018_paper.pdf | Super-Identity Convolutional Neural Networkfor Face Hallucination |
| 285 | Kaiyue_Lu_Deep_Texture_and_ECCV_2018_paper.pdf | Deep Texture and Structure Aware FilteringNetwork for Image Smoothing |
| 286 | Kaiyue_Pang_Deep_Factorised_Inverse-Sketching_ECCV_2018_paper.pdf | Deep Factorised Inverse-Sketching |
| 287 | Kai_Xu_LAPCSRA_Deep_Laplacian_ECCV_2018_paper.pdf | LAPRAN: A Scalable Laplacian PyramidReconstructive Adversarial Network for FlexibleCompressive Sensing Reconstruction |
| 288 | Kang_Pairwise_Relational_Networks_ECCV_2018_paper.pdf | Pairwise Relational Networks for FaceRecognition |
| 289 | Karim_Ahmed_MaskConnect_Connectivity_Learning_ECCV_2018_paper.pdf | MaskConnect: Connectivity Learning byGradient Descent |
| 290 | Keisuke_Tateno_Distortion-Aware_Convolutional_Filters_ECCV_2018_paper.pdf | Distortion-Aware Convolutional Filters for DensePrediction in Panoramic Images |
| 291 | Keizo_Kato_Compositional_Learning_of_ECCV_2018_paper.pdf | Compositional Learningfor Human Ob ject Interaction |
| 292 | Kejie_Li_Efficient_Dense_Point_ECCV_2018_paper.pdf | Efficient Dense Point Cloud Ob jectReconstruction Using Deformation Vector Fields |
| 293 | Kemal_Oksuz_Localization_Recall_Precision_ECCV_2018_paper.pdf | Localization Recall Precision (LRP): A NewPerformance Metric for Ob ject Detection |
| 294 | Keren_Ye_ADVISE_Symbolism_and_ECCV_2018_paper.pdf | ADVISE: Symbolism and External Knowledgefor Decoding Advertisements |
| 295 | Ke_Gong_Instance-level_Human_Parsing_ECCV_2018_paper.pdf | Instance-level Human Parsing via Part GroupingNetwork |
| 296 | Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.pdf | Universal Sketch Perceptual Grouping |
| 297 | Ke_Zhang_Retrospective_Encoders_for_ECCV_2018_paper.pdf | Retrospective Encodersfor Video Summarization |
| 298 | Kim_SAN_Learning_Relationship_ECCV_2018_paper.pdf | SAN: Learning Relationship betweenConvolutional Featuresfor Multi-Scale Ob ject Detection |
| 299 | Kohei_Uehara_Visual_Question_Generation_ECCV_2018_paper.pdf | Visual Question Generation forClass Acquisition of Unknown Ob jects |
| 300 | Konda_Reddy_Mopuri_Ask_Acquire_and_ECCV_2018_paper.pdf | Ask, Acquire, and Attack: Data-free UAPGeneration using Class Impressions |
| 301 | Konstantinos-Nektarios_Lianos_VSO_Visual_Semantic_ECCV_2018_paper.pdf | VSO: Visual Semantic Odometry |
| 302 | Konstantin_Shmelkov_How_good_is_ECCV_2018_paper.pdf | How good is my GAN? |
| 303 | Ko_Nishino_Variable_Ring_Light_ECCV_2018_paper.pdf | Variable Ring Light Imaging:Capturing Transient Subsurface Scattering withAn Ordinary Camera |
| 304 | Kripasindhu_Sarkar_Learning_3D_shapes_ECCV_2018_paper.pdf | Learning 3D Shapes as Multi-LayeredHeight-maps using 2D Convolutional Networks |
| 305 | Krishna_Kumar_Singh_Transferring_Common-Sense_Knowledge_ECCV_2018_paper.pdf | DOCK: Detecting Ob jectsby transferring Common-sense Knowledge |
| 306 | Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper.pdf | Zero-Shot Deep Domain Adaptation |
| 307 | Kuang-Huei_Lee_Stacked_Cross_Attention_ECCV_2018_paper.pdf | Stacked Cross Attention forImage-Text Matching |
| 308 | Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.pdf | Unsupervised CNN-based Co-Saliency Detectionwith Graphical Optimization |
| 309 | Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.pdf | Open Set Domain Adaptation byBackpropagation |
| 310 | Kyoungoh_Lee_Propagating_LSTM_3D_ECCV_2018_paper.pdf | Propagating LSTM: 3D Pose Estimationbased on Joint Interdependency |
| 311 | Kyungmin_Kim_Multimodal_Dual_Attention_ECCV_2018_paper.pdf | Multimodal Dual Attention Memory forVideo Story Question Answering. |
| 312 | Lai_Jiang_DeepVS_A_Deep_ECCV_2018_paper.pdf | DeepVS: A Deep Learning Based Video SaliencyPrediction Approach |
| 313 | Lan_Wang_PM-GANs_Discriminative_Representation_ECCV_2018_paper.pdf | PM-GANs: Discriminative RepresentationLearning for Action Recognition UsingPartial-modalities |
| 314 | Lawrence_Neal_Open_Set_Learning_ECCV_2018_paper.pdf | Open Set Learning with Counterfactual Images |
| 315 | Lei_Chen_Part-Activated_Deep_Reinforcement_ECCV_2018_paper.pdf | Part-Activated Deep Reinforcement Learning forAction Prediction |
| 316 | Lei_Zhou_Learning_and_Matching_ECCV_2018_paper.pdf | Learning and Matching Multi-View Descriptorsfor Registration of Point Clouds |
| 317 | Lei_Zhu_Bi-directional_Feature_Pyramid_ECCV_2018_paper.pdf | Bidirectional Feature Pyramid Network withRecurrent Attention Residual Modulesfor Shadow Detection |
| 318 | Lele_Chen_Lip_Movements_Generation_ECCV_2018_paper.pdf | Lip Movements Generation at a Glance |
| 319 | Lequan_Yu_EC-Net_an_Edge-aware_ECCV_2018_paper.pdf | EC-Net: an Edge-aware Point setConsolidation Network |
| 320 | Liang-Chieh_Chen_Encoder-Decoder_with_Atrous_ECCV_2018_paper.pdf | Encoder-Decoder with Atrous SeparableConvolution for Semantic Image Segmentation |
| 321 | Liangliang_Ren_Collaborative_Deep_Reinforcement_ECCV_2018_paper.pdf | Collaborative Deep Reinforcement Learning forMulti-Ob ject Tracking |
| 322 | Liangliang_Ren_Deep_Reinforcement_Learning_ECCV_2018_paper.pdf | Deep Reinforcement Learning with IterativeShift for Visual Tracking |
| 323 | Liangyan_Gui_Adversarial_Geometry-Aware_Human_ECCV_2018_paper.pdf | Adversarial Geometry-Aware Human Motion Prediction |
| 324 | Liangyan_Gui_Few-Shot_Human_Motion_ECCV_2018_paper.pdf | Few-Shot Human Motion Prediction viaMeta-Learning |
| 325 | Liang_Generative_Semantic_Manipulation_ECCV_2018_paper.pdf | Generative Semantic Manipulation withMask-Contrasting GAN |
| 326 | Liang_Mi_Variational_Wasserstein_Clustering_ECCV_2018_paper.pdf | Variational Wasserstein Clustering |
| 327 | Ligeng_Zhu_Sparsely_Aggregated_Convolutional_ECCV_2018_paper.pdf | Sparsely Aggregated Convolutional Networks |
| 328 | Linchao_Zhu_Compound_Memory_Networks_ECCV_2018_paper.pdf | Compound Memory Networks forFew-shot Video Classification |
| 329 | Lingjie_Zhu_Large_Scale_Urban_ECCV_2018_paper.pdf | Large Scale Urban Scene Modeling from MVSMeshes |
| 330 | Lingyu_Wei_Real-Time_Hair_Rendering_ECCV_2018_paper.pdf | Real-Time Hair Rendering using SequentialAdversarial Networks |
| 331 | Lior_Talker_Efficient_Sliding_Window_ECCV_2018_paper.pdf | Efficient Sliding Window Computation forNN-Based Template Matching |
| 332 | Lior_Wolf_Estimating_the_Success_ECCV_2018_paper.pdf | Estimating the Success of UnsupervisedImage to Image Translation |
| 333 | Lipeng_Ke_Multi-Scale_Structure-Aware_Network_ECCV_2018_paper.pdf | Multi-Scale Structure-Aware Network forHuman Pose Estimation |
| 334 | Liren_Chen_The_Devil_of_ECCV_2018_paper.pdf | The Devil of Face Recognition is in the Noise |
| 335 | Lisa_Anne_Hendricks_Grounding_Visual_Explanations_ECCV_2018_paper.pdf | Grounding Visual Explanations |
| 336 | Lisa_Anne_Hendricks_Women_also_Snowboard_ECCV_2018_paper.pdf | Women Also Snowboard:Overcoming Bias in Captioning Models |
| 337 | Liuhao_Ge_Point-to-Point_Regression_PointNet_ECCV_2018_paper.pdf | Point-to-Point Regression PointNet for3D Hand Pose Estimation |
| 338 | Lixiong_Chen_Polarimetric_Three-View_Geometry_ECCV_2018_paper.pdf | Polarimetric Three-View Geometry |
| 339 | Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.pdf | GAL: Geometric Adversarial Loss forSingle-View 3D-Ob ject Reconstruction |
| 340 | Lluis_Gomez_Single_Shot_Scene_ECCV_2018_paper.pdf | Single Shot Scene Text Retrieval |
| 341 | Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.pdf | Learning to Forecast and Refine ResidualMotion for Image-to-Video Generation |
| 342 | Luona_Yang_Real-to-Virtual_Domain_Uni_ECCV_2018_paper.pdf | Real-to-Virtual Domain Unification for End-to-EndAutonomous Driving |
| 343 | Mang_YE_Robust_Anchor_Embedding_ECCV_2018_paper.pdf | Robust Anchor Embedding for UnsupervisedVideo Person Re-Identification in the Wild |
| 344 | Marc_Oliu_Folded_Recurrent_Neural_ECCV_2018_paper.pdf | Folded Recurrent Neural Networks for FutureVideo Prediction |
| 345 | Maria_Klodt_Supervising_the_new_ECCV_2018_paper.pdf | Supervising the new with the old: learning SFMfrom SFM |
| 346 | Marie-Morgane_Paumard_Image_Reassembly_Combining_ECCV_2018_paper.pdf | Image Reassembly Combining Deep Learningand Shortest Path Problem |
| 347 | Mariya_Vasileva_Learning_Type-Aware_Embeddings_ECCV_2018_paper.pdf | Learning Type-Aware Embeddings for FashionCompatibility |
| 348 | Markus_Oberweger_Making_Deep_Heatmaps_ECCV_2018_paper.pdf | Making Deep Heatmaps Robust to PartialOcclusions for 3D Ob ject Pose Estimation |
| 349 | Martin_Sundermeyer_Implicit_3D_Orientation_ECCV_2018_paper.pdf | Implicit 3D Orientation Learning for6D Ob ject Detection from RGB Images |
| 350 | Martyushev_Self-Calibration_of_Cameras_ECCV_2018_paper.pdf | Self-Calibration of Cameras with EuclideanImage Plane in Case of Two Views and KnownRelative Rotation Angle |
| 351 | Marzieh_Edraki_Generalized_Loss-Sensitive_Adversarial_ECCV_2018_paper.pdf | Generalized Loss-Sensitive Adversarial Learningwith Manifold Margins |
| 352 | Mateusz_Malinowski_Learning_Visual_Question_ECCV_2018_paper.pdf | Learning Visual Question Answering byBootstrapping Hard Attention |
| 353 | Mathieu_Garon_A_Framework_for_ECCV_2018_paper.pdf | A Framework for Evaluating6-DOF Ob ject Trackers |
| 354 | Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.pdf | Deep Clustering for Unsupervised Learningof Visual Features |
| 355 | Matteo_Fabbri_Learning_to_Detect_ECCV_2018_paper.pdf | Learning to Detect and Track Visible andOccluded Body Joints in a Virtual World |
| 356 | Matthew_Trager_On_the_Solvability_ECCV_2018_paper.pdf | On the Solvability of Viewing Graphs |
| 357 | Matthew_Trumble_Deep_Autoencoder_for_ECCV_2018_paper.pdf | Deep Autoencoder for Combined Human PoseEstimation and Body Model Upscaling |
| 358 | Matthias_Kummerer_Saliency_Benchmarking_Made_ECCV_2018_paper.pdf | Saliency Benchmarking Made Easy:Separating Models, Maps and Metrics |
| 359 | Matthias_Muller_TrackingNet_A_Large-Scale_ECCV_2018_paper.pdf | TrackingNet: A Large-Scale Dataset andBenchmark for Ob ject Tracking in the Wild ∗ |
| 360 | Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.pdf | Straight to the Facts: Learning Knowledge BaseRetrieval for Factual Visual Question Answering |
| 361 | Meiguang_Jin_Normalized_Blind_Deconvolution_ECCV_2018_paper.pdf | Normalized Blind Deconvolution |
| 362 | Melih_Engin_DeepKSPD_Learning_Kernel-matrix-based_ECCV_2018_paper.pdf | DeepKSPD: Learning Kernel-matrix-based SPDRepresentation for Fine-grained ImageRecognition |
| 363 | mengdan_zhang_Visual_Tracking_via_ECCV_2018_paper.pdf | Visual Tracking via Spatially AlignedCorrelation Filters Network |
| 364 | Mengshi_Qi_stagNet_An_Attentive_ECCV_2018_paper.pdf | stagNet: An Attentive Semantic RNNfor Group Activity Recognition |
| 365 | Meng_Tang_On_Regularized_Losses_ECCV_2018_paper.pdf | On Regularized Lossesfor Weakly-supervised CNN Segmentation |
| 366 | Meredith_Hu_Understanding_Perceptual_and_ECCV_2018_paper.pdf | Understanding Perceptual and ConceptualFluency at a Large Scale |
| 367 | Mian_Wei_Coded_Two-Bucket_Cameras_ECCV_2018_paper.pdf | Coded Two-Bucket Cameras for Computer Vision |
| 368 | Michael_Moeller_Lifting_Layers_Analysis_ECCV_2018_paper.pdf | Lifting Layers: Analysis and Applications |
| 369 | Michal_Polic_Fast_and_Precise_ECCV_2018_paper.pdf | Fast and Accurate Camera CovarianceComputation for Large 3D Reconstruction |
| 370 | Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf | Dynamic Task Prioritization for MultitaskLearning |
| 371 | Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf | Neural Graph Matching Networks for Fewshot3D Action Recognition |
| 372 | Michitaka_Yoshida_Joint_optimization_for_ECCV_2018_paper.pdf | Joint optimization for compressive video sensingand reconstruction under hardware constraints |
| 373 | Miika_Aittala_Burst_Image_Deblurring_ECCV_2018_paper.pdf | Burst Image Deblurring Using PermutationInvariant Convolutional Neural Networks |
| 374 | Mikael_Persson_Lambda_Twist_An_ECCV_2018_paper.pdf | Lambda Twist: An Accurate Fast RobustPerspective Three Point (P3P) Solver. |
| 375 | Mingfei_Gao_C-WSL_Count-guided_Weakly_ECCV_2018_paper.pdf | C-WSL: Count-guided Weakly SupervisedLocalization |
| 376 | Minghao_Guo_Dual-Agent_Deep_Reinforcement_ECCV_2018_paper.pdf | Dual-Agent Deep Reinforcement Learning forDeformable Face Tracking |
| 377 | Mingtao_Feng_3D_Face_Reconstruction_ECCV_2018_paper.pdf | 3D Face Reconstruction from Light Field Images:A Model-free Approach |
| 378 | Mingze_Xu_Joint_Person_Segmentation_ECCV_2018_paper.pdf | Joint Person Segmentation and Identification inSynchronized First- and Third-person Videos |
| 379 | Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf | Deep Continuous Fusion for Multi-Sensor 3DOb ject Detection |
| 380 | Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.pdf | Multi-Attention Multi-Class Constraint forFine-grained Image Recognition |
| 381 | Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.pdf | Teaching Machines to Understand Baseball Games:Large-Scale Baseball Video Database forMultiple Video Understanding Tasks |
| 382 | Minhyeok_Heo_Monocular_Depth_Estimation_ECCV_2018_paper.pdf | Monocular Depth Estimation Using Whole StripMasking and Reliability-Based Refinement |
| 383 | Minjun_Li_Unsupervised_Image-to-Image_Translation_ECCV_2018_paper.pdf | Unsupervised Image-to-Image Translation withStacked Cycle-Consistent Adversarial Networks |
| 384 | Minxian_Li_Unsupervised_Person_Re-identification_ECCV_2018_paper.pdf | Unsupervised Person Re-identification byDeep Learning Tracklet Association |
| 385 | Mir_Rayat_Imtiaz_Hossain_Exploiting_temporal_information_ECCV_2018_paper.pdf | Exploiting temporal information for 3D humanpose estimation |
| 386 | Mohammadreza_Zolfaghari_ECO_Efficient_Convolutional_ECCV_2018_paper.pdf | ECO: Efficient Convolutional Network forOnline Video Understanding |
| 387 | Mohammad_Tavakolian_Deep_Discriminative_Model_ECCV_2018_paper.pdf | Deep Discriminative Model for VideoClassification |
| 388 | Mohammed_Fathy_Hierarchical_Metric_Learning_ECCV_2018_paper.pdf | Hierarchical Metric Learning and Matching for2D and 3D Geometric Correspondences⋆ |
| 389 | Mohit_Gupta_A_Geometric_Perspective_ECCV_2018_paper.pdf | A Geometric Perspective on Structured Light Coding |
| 390 | Moitreya_Chatterjee_Diverse_and_Coherent_ECCV_2018_paper.pdf | Diverse and Coherent Paragraph Generationfrom Images |
| 391 | Mostafa_Ibrahim_Hierarchical_Relational_Networks_ECCV_2018_paper.pdf | Hierarchical Relational Networks for Group ActivityRecognition and Retrieval |
| 392 | Mrigank_Rochan_Video_Summarization_Using_ECCV_2018_paper.pdf | Video Summarization Using Fully ConvolutionalSequence Networks |
| 393 | Muhammed_Kocabas_MultiPoseNet_Fast_Multi-Person_ECCV_2018_paper.pdf | MultiPoseNet: Fast Multi-Person PoseEstimation using Pose Residual Network |
| 394 | Myunggi_Lee_Motion_Feature_Network_ECCV_2018_paper.pdf | Motion Feature Network: Fixed Motion Filterfor Action Recognition |
| 395 | Naeha_Sharif_NNEval_Neural_Network_ECCV_2018_paper.pdf | NNEval: Neural Network based EvaluationMetric for Image Captioning |
| 396 | Namhyuk_Ahn_Fast_Accurate_and_ECCV_2018_paper.pdf | Fast, Accurate, and LightweightSuper-Resolutionwith Cascading Residual Network |
| 397 | Nanyang_Wang_Pixel2Mesh_Generating_3D_ECCV_2018_paper.pdf | Pixel2Mesh: Generating 3D Mesh Modelsfrom Single RGB Images |
| 398 | Nan_Yang_Deep_Virtual_Stereo_ECCV_2018_paper.pdf | Deep Virtual Stereo Odometry:Leveraging Deep Depth Prediction forMonocular Direct Sparse Odometry |
| 399 | Natalia_Neverova_Two_Stream__ECCV_2018_paper.pdf | Dense Pose Transfer |
| 400 | Nathan_Silberman_ExplainGAN_Model_Explanation_ECCV_2018_paper.pdf | ExplainGAN: Model Explanation via DecisionBoundary Crossing Transformations |
| 401 | Navaneeth_Bodla_Semi-supervised_FusedGAN_for_ECCV_2018_paper.pdf | Semi-supervised FusedGAN for ConditionalImage Generation |
| 402 | Ngoc-Trung_Tran_Generative_Adversarial_Autoencoder_ECCV_2018_paper.pdf | Dist-GAN: An Improved GAN using DistanceConstraints |
| 403 | Nicholas_Rhinehart_R2P2_A_ReparameteRized_ECCV_2018_paper.pdf | R2P2: A ReparameteRized Pushforward Policyfor Diverse, Precise Generative Path Forecasting |
| 404 | Niclas_Zeller_Scale-Awareness_of_Light_ECCV_2018_paper.pdf | Scale-Awareness of Light Field Camera basedVisual Odometry |
| 405 | NIKITA_DVORNIK_Modeling_Visual_Context_ECCV_2018_paper.pdf | Modeling Visual Context is Key toAugmenting Ob ject Detection Datasets |
| 406 | Nikolaos_Karianakis_Reinforced_Temporal_Attention_ECCV_2018_paper.pdf | Reinforced Temporal Attention and Split-RateTransfer for Depth-Based PersonRe-Identification |
| 407 | Nikolaos_Passalis_Learning_Deep_Representations_ECCV_2018_paper.pdf | Learning Deep Representations withProbabilistic Knowledge Transfer |
| 408 | Nikolaos_Sarafianos_Deep_Imbalanced_Attribute_ECCV_2018_paper.pdf | Deep Imbalanced Attribute Classification usingVisual Attention Aggregation |
| 409 | NIKOLAOS_ZIOULIS_OmniDepth_Dense_Depth_ECCV_2018_paper.pdf | OmniDepth: Dense Depth Estimation forIndoors Spherical Panoramas. |
| 410 | Nimisha_T_M_Unsupervised_Class-Specific_Deblurring_ECCV_2018_paper.pdf | Unsupervised Class-Specific Deblurring |
| 411 | Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf | ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design |
| 412 | Ning_Xu_YouTube-VOS_Sequence-to-Sequence_Video_ECCV_2018_paper.pdf | YouTube-VOS: Sequence-to-Sequence VideoOb ject Segmentation |
| 413 | Nuno_Garcia_Modality_Distillation_with_ECCV_2018_paper.pdf | Modality Distillation with Multiple StreamNetworks for Action Recognition |
| 414 | Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper.pdf | ShapeStacks: Learning Vision-Based PhysicalIntuition for Generalised Ob ject Stacking |
| 415 | Oliver_Zendel_WildDash_-_Creating_ECCV_2018_paper.pdf | WildDash - Creating Hazard-Aware Benchmarks |
| 416 | Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.pdf | X2Face: A network for controlling facegeneration using images, audio, and pose codes |
| 417 | Panna_Felsen_Where_Will_They_ECCV_2018_paper.pdf | Where Will They Go? Predicting Fine-GrainedAdversarial Multi-Agent Motion usingConditional Variational Autoencoders |
| 418 | Parikshit_Sakurikar_Single_Image_Scene_ECCV_2018_paper.pdf | RefocusGAN: Scene Refocusing usinga Single Image |
| 419 | Patrick_Follmann_D2S_Densely_Segmented_ECCV_2018_paper.pdf | MVTec D2S: Densely Segmented SupermarketDataset |
| 420 | Patrick_Wieschollek_Separating_Reflection_and_ECCV_2018_paper.pdf | Separating Reflection and Transmission Imagesin the Wild |
| 421 | Pauline_Luc_Predicting_Future_Instance_ECCV_2018_paper.pdf | Predicting Future Instance Segmentationby Forecasting Convolutional Features |
| 422 | Paul_Hongsuck_Seo_Attentive_Semantic_Alignment_ECCV_2018_paper.pdf | Attentive Semantic Alignmentwith Offset-Aware Correlation Kernels |
| 423 | Paul_Hongsuck_Seo_Enhancing_Image_Geolocalization_ECCV_2018_paper.pdf | CPlaNet: Enhancing Image Geolocalizationby Combinatorial Partitioning of Maps |
| 424 | Pau_Rodriguez_Lopez_Attend_and_Rectify_ECCV_2018_paper.pdf | Attend and Rectify: a Gated AttentionMechanism for Fine-Grained Recovery. |
| 425 | Pedro_Miraldo_A_Minimal_Closed-Form_ECCV_2018_paper.pdf | A Minimal Closed-Form Solution forMulti-Perspective Pose Estimationusing Points and Lines |
| 426 | Peiliang_LI_Stereo_Vision-based_Semantic_ECCV_2018_paper.pdf | Stereo Vision-based Semantic 3D Ob ject andEgo-motion Tracking for Autonomous Driving |
| 427 | Pei_Wang_Towards_Realistic_Predictors_ECCV_2018_paper.pdf | Towards Realistic Predictors |
| 428 | Pengfei_Zhang_Adding_Attentiveness_to_ECCV_2018_paper.pdf | Adding Attentiveness to the Neurons inRecurrent Neural Networks |
| 429 | Pengyuan_Lyu_Mask_TextSpotter_An_ECCV_2018_paper.pdf | Mask TextSpotter: An End-to-End TrainableNeural Network for Spotting Text withArbitrary Shapes |
| 430 | Peng_Tang_Weakly_Supervised_Region_ECCV_2018_paper.pdf | Weakly Supervised Region Proposal Networkand Ob ject Detection |
| 431 | Pierre_Stock_ConvNets_and_ImageNet_ECCV_2018_paper.pdf | ConvNets and ImageNet Beyond Accuracy:Understanding Mistakes and Uncovering Biases |
| 432 | Piotr_Koniusz_Museum_Exhibit_Identification_ECCV_2018_paper.pdf | Museum Exhibit Identification Challenge for theSupervised Domain Adaptation and Beyond |
| 433 | Po-Yu_Huang_Efficient_Uncertainty_Estimation_ECCV_2018_paper.pdf | Efficient Uncertainty Estimation for SemanticSegmentation in Videos |
| 434 | Pradeep_Kumar_Jayaraman_Quadtree_Convolutional_Neural_ECCV_2018_paper.pdf | Quadtree Convolutional Neural Networks |
| 435 | Pyojin_Kim_Linear_RGB-D_SLAM_ECCV_2018_paper.pdf | Linear RGB-D SLAM for Planar Environments |
| 436 | Qiang_Qiu_ForestHash_Semantic_Hashing_ECCV_2018_paper.pdf | ForestHash: Semantic Hashing With Shallow RandomForests and Tiny Convolutional Networks |
| 437 | Qianru_Sun_A_Hybrid_Model_ECCV_2018_paper.pdf | A Hybrid Model for Identity Obfuscation byFace Replacement |
| 438 | Qinghao_Hu_Training_Binary_Weight_ECCV_2018_paper.pdf | Training Binary Weight Networks viaSemi-Binary Decomposition |
| 439 | Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper.pdf | Decouple Learning for Parameterized ImageOperators |
| 440 | Qingqiu_Huang_Person_Search_in_ECCV_2018_paper.pdf | Person Search in Videos with One PortraitThrough Visual and Temporal Links |
| 441 | Qingyi_Tao_Zero-Annotation_Object_Detection_ECCV_2018_paper.pdf | Zero-Annotation Ob ject Detection with WebKnowledge Transfer |
| 442 | Qing_Li_VQA-E_Explaining_Elaborating_ECCV_2018_paper.pdf | VQA-E: Explaining, Elaborating, and EnhancingYour Answers for Visual Questions |
| 443 | Qixing_Huang_Joint_Map_and_ECCV_2018_paper.pdf | Joint Map and Symmetry Synchronization |
| 444 | Qi_Guo_Tackling_3D_ToF_ECCV_2018_paper.pdf | Tackling 3D ToF Artifacts Through Learningand the FLAT Dataset |
| 445 | Qi_Ye_Occlusion-aware_Hand_Pose_ECCV_2018_paper.pdf | Occlusion-aware Hand Pose Estimation UsingHierarchical Mixture Density Network |
| 446 | Quanlong_Zheng_Task-driven_Webpage_Saliency_ECCV_2018_paper.pdf | Task-driven Webpage Saliency |
| 447 | RAFAEL_FELIX_Multi-modal_Cycle-consistent_Generalized_ECCV_2018_paper.pdf | Multi-modal Cycle-consistent GeneralizedZero-Shot Learning |
| 448 | Rahaf_Aljundi_Memory_Aware_Synapses_ECCV_2018_paper.pdf | Memory Aware Synapses: Learning what (not) to forget |
| 449 | Rajendra_Nagar_Fast_and_Accurate_ECCV_2018_paper.pdf | Fast and Accurate Intrinsic Symmetry Detection |
| 450 | Rajvi_Shah_View-graph_Selection_Framework_ECCV_2018_paper.pdf | View-graph Selection Framework for SfM |
| 451 | Rameswar_Panda_Contemplating_Visual_Emotions_ECCV_2018_paper.pdf | Contemplating Visual Emotions: Understandingand Overcoming Dataset Bias |
| 452 | Ramprasaath_Ramasamy_Selvaraju_Choose_Your_Neuron_ECCV_2018_paper.pdf | Choose Your Neuron: Incorporating DomainKnowledge through Neuron-Importance |
| 453 | Relja_Arandjelovic_Objects_that_Sound_ECCV_2018_paper.pdf | Ob jects that Sound |
| 454 | Rene_Ranftl_Deep_Fundamental_Matrix_ECCV_2018_paper.pdf | Deep Fundamental Matrix Estimation |
| 455 | Renjiao_Yi_Faces_as_Lighting_ECCV_2018_paper.pdf | Faces as Lighting Probes via Unsupervised DeepHighlight Extraction |
| 456 | Rex_Yue_Wu_BusterNet_Detecting_Copy-Move_ECCV_2018_paper.pdf | BusterNet: Detecting Copy-Move Image Forgerywith Source/Target Localization |
| 457 | Rishabh_Dabral_Learning_3D_Human_ECCV_2018_paper.pdf | Learning 3D Human Pose from Structure and Motion |
| 458 | Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.pdf | A Deeply-initialized Coarse-to-fine Ensemble ofRegression Trees for Face Alignment |
| 459 | Roey_Mechrez_The_Contextual_Loss_ECCV_2018_paper.pdf | The Contextual Loss for Image Transformationwith Non-Aligned Data |
| 460 | Rohit_Pandey_Efficient_6-DoF_Tracking_ECCV_2018_paper.pdf | Efficient 6-DoF Tracking of Handheld Ob jectsfrom an Egocentric Viewpoint |
| 461 | Ronald_Clark_Neural_Nonlinear_least_ECCV_2018_paper.pdf | Learning to Solve Nonlinear Least Squaresfor Monocular Stereo |
| 462 | Ronghang_Hu_Explainable_Neural_Computation_ECCV_2018_paper.pdf | Explainable Neural Computation via StackNeural Module Networks |
| 463 | Rui_Yu_Hard-Aware_Point-to-Set_Deep_ECCV_2018_paper.pdf | Hard-Aware Point-to-Set Deep Metric forPerson Re-identification |
| 464 | Ruochen_Fan_Associating_Inter-Image_Salient_ECCV_2018_paper.pdf | Associating Inter-Image Salient Instances for WeaklySupervised Semantic Segmentation |
| 465 | Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.pdf | Learning to Separate Ob ject Sounds byWatching Unlabeled Video |
| 466 | Ruohan_Zhang_AGIL_Learning_Attention_ECCV_2018_paper.pdf | AGIL: Learning Attention from Human forVisuomotor Tasks |
| 467 | Ruoteng_Li_Robust_Optical_Flow_ECCV_2018_paper.pdf | Robust Optical Flow in Rainy Scenes⋆ |
| 468 | Ruoxi_Deng_Learning_to_Predict_ECCV_2018_paper.pdf | Learning to Predict Crisp Boundaries |
| 469 | Sachin_Mehta_ESPNet_Efficient_Spatial_ECCV_2018_paper.pdf | ESPNet: Efficient Spatial Pyramid of DilatedConvolutions for Semantic Segmentation |
| 470 | Safa_Cicek_SaaS_Speed_as_ECCV_2018_paper.pdf | SaaS: Speed as a Supervisorfor Semi-supervised Learning |
| 471 | Safa_Messaoud_Structural_Consistency_and_ECCV_2018_paper.pdf | Structural Consistency and Controllability forDiverse Colorization |
| 472 | Saihui_Hou_Progressive_Lifelong_Learning_ECCV_2018_paper.pdf | Lifelong Learning viaProgressive Distillation and Retrospection |
| 473 | Saining_Xie_Rethinking_Spatiotemporal_Feature_ECCV_2018_paper.pdf | Rethinking Spatiotemporal Feature Learning:Speed-Accuracy Trade-offs in Video Classification |
| 474 | Sameh_Khamis_StereoNet_Guided_Hierarchical_ECCV_2018_paper.pdf | StereoNet: Guided Hierarchical Refinement forReal-Time Edge-Aware Depth Prediction |
| 475 | Samuel_Albanie_Learnable_PINs_Cross-Modal_ECCV_2018_paper.pdf | Learnable PINs: Cross-Modal Embeddings forPerson Identity |
| 476 | Samuel_Albanie_Semi-convolutional_Operators_for_ECCV_2018_paper.pdf | Semi-convolutional Operators forInstance Segmentation |
| 477 | Samuel_Schulter_Learning_to_Look_ECCV_2018_paper.pdf | Learning to Look around Ob jects forTop-View Representations of Outdoor Scenes |
| 478 | Sanghyun_Son_Clustering_Kernels_for_ECCV_2018_paper.pdf | Clustering Convolutional Kernels to CompressDeep Neural Networks |
| 479 | Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.pdf | CBAM: Convolutional Block Attention Module |
| 480 | Sangryul_Jeon_PARN_Pyramidal_Affine_ECCV_2018_paper.pdf | PARN: Pyramidal Affine Regression Networksfor Dense Semantic Correspondence |
| 481 | Santhosh_Kumar_Ramakrishnan_Sidekick_Policy_Learning_ECCV_2018_paper.pdf | Sidekick Policy Learningfor Active Visual Exploration |
| 482 | Santiago_Cadena_Diverse_feature_visualizations_ECCV_2018_paper.pdf | Diverse feature visualizations reveal invariancesin early layers of deep neural networks |
| 483 | Santiago_Cortes_ADVIO_An_Authentic_ECCV_2018_paper.pdf | ADVIO: An Authentic Dataset forVisual-Inertial Odometry |
| 484 | Sasikiran_Yelamarthi_A_Zero-Shot_Framework_ECCV_2018_paper.pdf | A Zero-Shot Framework for Sketch Based ImageRetrieval. |
| 485 | Satwik_Kottur_Visual_Coreference_Resolution_ECCV_2018_paper.pdf | Visual Coreference Resolution in Visual Dialogusing Neural Module Networks |
| 486 | Sebastian_Bullinger_3D_Vehicle_Trajectory_ECCV_2018_paper.pdf | 3D Vehicle Tra jectory Reconstruction inMonocular Video Data Using EnvironmentStructure Constraints |
| 487 | Sekii_Pose_Proposal_Networks_ECCV_2018_paper.pdf | Pose Proposal Networks |
| 488 | Seong-Jin_Park_SRFeat_Single_Image_ECCV_2018_paper.pdf | SRFeat: Single Image Super-Resolutionwith Feature Discrimination |
| 489 | Seong_Tae_Kim_Facial_Dynamics_Interpreter_ECCV_2018_paper.pdf | Facial Dynamics Interpreter Network: What arethe Important Relations between LocalDynamics for Facial Trait Estimation? |
| 490 | Seonwook_Park_Deep_Pictorial_Gaze_ECCV_2018_paper.pdf | Deep Pictorial Gaze Estimation |
| 491 | Sergey_Prokudin_Deep_Directional_Statistics_ECCV_2018_paper.pdf | Deep Directional Statistics:Pose Estimation withUncertainty Quantification |
| 492 | Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.pdf | License Plate Detection and Recognition inUnconstrained Scenarios |
| 493 | Seung-Wook_Kim_Parallel_Feature_Pyramid_ECCV_2018_paper.pdf | Parallel Feature Pyramid Network for Ob jectDetection |
| 494 | SEUNG_HYUN_LEE_Self-supervised_Knowledge_Distillation_ECCV_2018_paper.pdf | Self-supervised Knowledge Distillation UsingSingular Value Decomposition |
| 495 | shaifali_parashar_Self-Calibrating_Isometric__ECCV_2018_paper.pdf | Self-Calibrating IsometricNon-Rigid Structure-from-Motion |
| 496 | Shangbang_Long_TextSnake_A_Flexible_ECCV_2018_paper.pdf | TextSnake: A Flexible Representation forDetecting Text of Arbitrary Shapes |
| 497 | Shangzhe_Wu_Deep_High_Dynamic_ECCV_2018_paper.pdf | Deep High Dynamic Range Imaging with LargeForeground Motions |
| 498 | Shao-Hua_Sun_Multi-view_to_Novel_ECCV_2018_paper.pdf | Multi-view to Novel view:Synthesizing novel views with Self-LearnedConfidence |
| 499 | Shaofei_Wang_Accelerating_Dynamic_Programs_ECCV_2018_paper.pdf | Accelerating Dynamic Programs via NestedBenders Decomposition with Application toMulti-Person Pose Estimation |
| 500 | Sheng-Wei_Huang_AugGAN_Cross_Domain_ECCV_2018_paper.pdf | AugGAN: Cross Domain Adaptation withGAN-based Data Augmentation |
| 501 | Sheng_Guo_CurriculumNet_Learning_from_ECCV_2018_paper.pdf | CurriculumNet: Weakly Supervised Learningfrom Large-Scale Web Images |
| 502 | Shervin_Ardeshir_Integrating_Egocentric_Videos_ECCV_2018_paper.pdf | Integrating Egocentric Videos in Top-view SurveillanceVideos: Joint Identification and Temporal Alignment |
| 503 | Shifeng_Zhang_Occlusion-aware_R-CNN_Detecting_ECCV_2018_paper.pdf | Occlusion-aware R-CNN:Detecting Pedestrians in a Crowd |
| 504 | Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.pdf | Specular-to-Diffuse Translation forMulti-View Reconstruction |
| 505 | Shitala_Prasad_Using_Object_Information_ECCV_2018_paper.pdf | Using Ob ject Information for Spotting Text |
| 506 | Shivanthan_Yohanandan_Saliency_Preservation_in_ECCV_2018_paper.pdf | Saliency Preservation in Low-Resolution GrayscaleImages |
| 507 | Shiyao_Wang_Fully_Motion-Aware_Network_ECCV_2018_paper.pdf | Fully Motion-Aware Network for Video Ob jectDetection |
| 508 | Shi_Chen_Boosted_Attention_Leveraging_ECCV_2018_paper.pdf | Boosted Attention: Leveraging HumanAttention for Image Captioning |
| 509 | shi_jin_Learning_to_Dodge_ECCV_2018_paper.pdf | Learning to Dodge A Bullet:Concyclic View Morphing via Deep Learning |
| 510 | Shi_Yan_DDRNet_Depth_Map_ECCV_2018_paper.pdf | DDRNet: Depth Map Denoising and Refinementfor Consumer Depth Cameras UsingCascaded CNNs |
| 511 | Shuangjun_Liu_Inner_Space_Preserving_ECCV_2018_paper.pdf | Inner Space Preserving Generative PoseMachine |
| 512 | Shubham_Tulsiani_Layer-structured_3D_Scene_ECCV_2018_paper.pdf | Layer-structured 3D Scene Inferencevia View Synthesis |
| 513 | Shuhan_Chen_Reverse_Attention_for_ECCV_2018_paper.pdf | Reverse Attention for Salient Ob ject Detection |
| 514 | Siddharth_Tourani_MPLP_Fast_Parallel_ECCV_2018_paper.pdf | MPLP++: Fast, Parallel Dual Block-Coordinate Ascentfor Dense Graphical Models |
| 515 | Sifei_Liu_Switchable_Temporal_Propagation_ECCV_2018_paper.pdf | Switchable Temporal Propagation Network |
| 516 | Sijia_Cai_Weakly-supervised_Video_Summarization_ECCV_2018_paper.pdf | Weakly-supervised Video Summarization usingVariational Encoder-Decoder and Web Prior |
| 517 | Simon_Hecker_Learning_to_Drive_ECCV_2018_paper.pdf | End-to-End Learning of Driving Models withSurround-View Cameras and Route Planners |
| 518 | Simon_Jenni_Deep_Bilevel_Learning_ECCV_2018_paper.pdf | Deep Bilevel Learning |
| 519 | Simyung_Chang_Broadcasting_Convolutional_Network_ECCV_2018_paper.pdf | Broadcasting Convolutional Networkfor Visual Relational Reasoning |
| 520 | Siqi_Liu_Remote_Photoplethysmography_Correspondence_ECCV_2018_paper.pdf | Remote Photoplethysmography CorrespondenceFeature for 3D Mask Face Presentation AttackDetection |
| 521 | Siqi_Yang_Using_LIP_to_ECCV_2018_paper.pdf | Using LIP to Gloss Over Faces in Single-StageFace Detection Networks |
| 522 | Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.pdf | Unsupervised Video Ob ject Segmentation withMotion-based Bilateral Networks |
| 523 | Siyeong_Lee_Deep_Recursive_HDRI_ECCV_2018_paper.pdf | Deep Recursive HDRI: Inverse Tone Mappingusing Generative Adversarial Networks |
| 524 | Siyuan_Huang_Monocular_Scene_Parsing_ECCV_2018_paper.pdf | Holistic 3D Scene Parsing and Reconstructionfrom a Single RGB Image |
| 525 | Siyuan_Qiao_Deep_Co-Training_for_ECCV_2018_paper.pdf | Deep Co-Training for Semi-SupervisedImage Recognition |
| 526 | Siyuan_Qi_Learning_Human-Object_Interactions_ECCV_2018_paper.pdf | Learning Human-Ob ject Interactions byGraph Parsing Neural Networks |
| 527 | Sizhuo_3D_Motion_Sensing_ECCV_2018_paper.pdf | 3D Scene Flow from 4D Light Field Gradients |
| 528 | Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.pdf | Domain Adaptation through Synthesis forUnsupervised Person Re-identification |
| 529 | Songtao_Liu_Receptive_Field_Block_ECCV_2018_paper.pdf | Receptive Field Block Net for Accurate and FastOb ject Detection |
| 530 | SouYoung_Jin_Unsupervised_Hard-Negative_Mining_ECCV_2018_paper.pdf | Unsupervised Hard Example Mining fromVideos for Improved Ob ject Detection |
| 531 | Steffen_Wolf_The_Mutex_Watershed_ECCV_2018_paper.pdf | The Mutex Watershed:Efficient, Parameter-Free Image Partitioning |
| 532 | Stephane_Lathuiliere_DeepGUM_Learning_Deep_ECCV_2018_paper.pdf | DeepGUM: Learning Deep Robust Regression with aGaussian-Uniform Mixture Model |
| 533 | Sujoy_Paul_W-TALC_Weakly-supervised_Temporal_ECCV_2018_paper.pdf | W-TALC: Weakly-supervised Temporal ActivityLocalization and Classification |
| 534 | Sunghun_Kang_Pivot_Correlational_Neural_ECCV_2018_paper.pdf | Pivot Correlational Neural Network forMultimodal Video Categorization |
| 535 | Tae-Hyun_Oh_Learning-based_Video_Motion_ECCV_2018_paper.pdf | Learning-based Video Motion Magnification |
| 536 | Tae_Hyun_Kim_Spatio-temporal_Transformer_Network_ECCV_2018_paper.pdf | Spatio-Temporal Transformer Networkfor Video Restoration |
| 537 | Taihong_Xiao_ELEGANT_Exchanging_Latent_ECCV_2018_paper.pdf | ELEGANT: Exchanging Latent Encodings withGAN for Transferring Multiple Face Attributes |
| 538 | Tal_Remez_Learning_to_Segment_ECCV_2018_paper.pdf | Learning to Segment via Cut-and-Paste |
| 539 | Tanmay_Gupta_Imagine_This_Scripts_ECCV_2018_paper.pdf | Imagine This! Scripts to Compositions to Videos |
| 540 | Tan_Yu_Product_Quantization_Network_ECCV_2018_paper.pdf | Product Quantization Network for Fast ImageRetrieval |
| 541 | Tao_Kong_Deep_Feature_Pyramid_ECCV_2018_paper.pdf | Deep Feature Pyramid Reconfiguration forOb ject Detection |
| 542 | Tao_Song_Small-scale_Pedestrian_Detection_ECCV_2018_paper.pdf | Small-scale Pedestrian Detection Based onTopological Line Localization and TemporalFeature Aggregation |
| 543 | Tat-Jun_Chin_Robust_fitting_in_ECCV_2018_paper.pdf | Robust Fitting in Computer Vision:Easy or Hard? |
| 544 | Tete_Xiao_Unified_Perceptual_Parsing_ECCV_2018_paper.pdf | Unified Perceptual Parsing for SceneUnderstanding |
| 545 | Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper.pdf | Zero-shot keyword spotting for visual speechrecognition in-the-wild |
| 546 | Thibault_Groueix_Shape_correspondences_from_ECCV_2018_paper.pdf | 3D-CODED : 3D Correspondences by DeepDeformation |
| 547 | Thomas_Holzmann_Semantically_Aware_Urban_ECCV_2018_paper.pdf | Semantically Aware Urban 3D Reconstructionwith Plane-Based Regularization |
| 548 | Thomas_Probst_Incremental_Non-Rigid_Structure-from-Motion_ECCV_2018_paper.pdf | Incremental Non-Rigid Structure-from-Motionwith Unknown Focal Length |
| 549 | Thomas_Probst_Model-free_Consensus_Maximization_ECCV_2018_paper.pdf | Model-free Consensus Maximizationfor Non-Rigid Shapes |
| 550 | Thomas_Robert_HybridNet_Classification_and_ECCV_2018_paper.pdf | HybridNet: Classification and ReconstructionCooperation for Semi-Supervised Learning |
| 551 | Tianfan_Xue_Seeing_Tree_Structure_ECCV_2018_paper.pdf | Seeing Tree Structure from Vibration |
| 552 | Tianlang_Chen_Factual_or_Emotional_ECCV_2018_paper.pdf | “Factual” or “Emotional”: Stylized ImageCaptioning with Adaptive Learning andAttention |
| 553 | Tianshu_Yu_Incremental_Multi-graph_Matching_ECCV_2018_paper.pdf | Incremental Multi-graph Matching via Diversityand Randomness based Graph Clustering |
| 554 | Tianwei_Lin_BSN_Boundary_Sensitive_ECCV_2018_paper.pdf | BSN: Boundary Sensitive Network for Temporal ActionProposal Generation |
| 555 | Tianyun_Zhang_A_Systematic_DNN_ECCV_2018_paper.pdf | A Systematic DNN Weight Pruning Frameworkusing Alternating Direction Method ofMultipliers |
| 556 | Tianyu_Yang_Learning_Dynamic_Memory_ECCV_2018_paper.pdf | Learning Dynamic Memory Networks for Ob jectTracking |
| 557 | Tian_Feng_Urban_Zoning_Using_ECCV_2018_paper.pdf | Urban Zoning Using Higher-Order Markov RandomFields on Multi-View Imagery Data |
| 558 | Tian_Ye_Interpretable_Intuitive_Physics_ECCV_2018_paper.pdf | Interpretable Intuitive Physics Model |
| 559 | Tien-Ju_Yang_NetAdapt_Platform-Aware_Neural_ECCV_2018_paper.pdf | NetAdapt: Platform-Aware Neural NetworkAdaptation for Mobile Applications |
| 560 | Timo_von_Marcard_Recovering_Accurate_3D_ECCV_2018_paper.pdf | Recovering Accurate 3D Human Pose in TheWild Using IMUs and a Moving Camera |
| 561 | Ting_Yao_Exploring_Visual_Relationship_ECCV_2018_paper.pdf | Exploring Visual Relationshipfor Image Captioning |
| 562 | Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.pdf | RT-GENE: Real-Time Eye Gaze Estimationin Natural Environments |
| 563 | Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper.pdf | PPF-FoldNet: Unsupervised Learning of RotationInvariant 3D Local Descriptors |
| 564 | Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper.pdf | BOP: Benchmark for 6D Ob ject Pose Estimation |
| 565 | Trung_Pham_Bayesian_Instance_Segmentation_ECCV_2018_paper.pdf | Bayesian Semantic Instance Segmentationin Open Set World |
| 566 | Tsung-Yu_Lin_Second-order_Democratic_Aggregation_ECCV_2018_paper.pdf | Second-order Democratic Aggregation |
| 567 | Tushar_Nagarajan_Attributes_as_Operators_ECCV_2018_paper.pdf | Attributes as Operators:Factorizing Unseen Attribute-Object Compositions |
| 568 | Tz-Ying_Wu_Liquid_Pouring_Monitoring_ECCV_2018_paper.pdf | Liquid Pouring Monitoring viaRich Sensory Inputs |
| 569 | T_M_Feroz_Ali_Maximum_Margin_Metric_ECCV_2018_paper.pdf | Maximum Margin Metric Learning Over DiscriminativeNullspace for Person Re-identification |
| 570 | Umar_Iqbal_Hand_Pose_Estimation_ECCV_2018_paper.pdf | Hand Pose Estimation via Latent 2.5D HeatmapRegression |
| 571 | Uta_Buchler_Improving_Spatiotemporal_Self-Supervision_ECCV_2018_paper.pdf | Improving Spatiotemporal Self-Supervisionby Deep Reinforcement Learning |
| 572 | Varun_Jampani_Superpixel_Sampling_Networks_ECCV_2018_paper.pdf | Superpixel Sampling Networks |
| 573 | Vassileios_Balntas_RelocNet_Continous_Metric_ECCV_2018_paper.pdf | RelocNet: Continuous Metric Learning Relocalisationusing Neural Nets |
| 574 | Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.pdf | Shape Reconstruction Using Volume Sweepingand Learned Photoconsistency |
| 575 | Viorica_Patraucean_Massively_Parallel_Video_ECCV_2018_paper.pdf | Massively Parallel Video Networks |
| 576 | Viresh_Ranjan_Iterative_Crowd_Counting_ECCV_2018_paper.pdf | Iterative Crowd Counting |
| 577 | Vivek_B_S_Gray_box_adversarial_ECCV_2018_paper.pdf | Gray-box Adversarial Training |
| 578 | Wayne_Wu_Learning_to_Reenact_ECCV_2018_paper.pdf | ReenactGAN: Learning to Reenact Facesvia Boundary Transfer |
| 579 | Wei-Chih_Hung_Learning_to_Blend_ECCV_2018_paper.pdf | Learning to Blend Photos |
| 580 | Wei-Chiu_Single_Image_Intrinsic_ECCV_2018_paper.pdf | Single Image Intrinsic Decomposition without a SingleIntrinsic Image |
| 581 | Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper.pdf | Learning Blind Video Temporal Consistency |
| 582 | Weidi_Xie_Comparator_Networks_ECCV_2018_paper.pdf | Comparator Networks |
| 583 | Weiwei_Shi_Transductive_Semi-Supervised_Deep_ECCV_2018_paper.pdf | Transductive Semi-Supervised Deep Learningusing Min-Max Features |
| 584 | Weixuan_Chen_DeepPhys_Video-Based_Physiological_ECCV_2018_paper.pdf | DeepPhys: Video-Based PhysiologicalMeasurement Using ConvolutionalAttention Networks |
| 585 | Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper.pdf | Depth-aware CNN for RGB-D Segmentation |
| 586 | Wei_Dong_Probabilistic_Signed_Distance_ECCV_2018_paper.pdf | PSDF Fusion: Probabilistic Signed DistanceFunction for On-the-fly 3D Data Fusion andScene Reconstruction |
| 587 | Wei_Liu_Learning_Efficient_Single-stage_ECCV_2018_paper.pdf | Learning Efficient Single-stage PedestrianDetectors by Asymptotic Localization Fitting |
| 588 | Wei_Tang_Deeply_Learned_Compositional_ECCV_2018_paper.pdf | Deeply Learned Compositional Models forHuman Pose Estimation |
| 589 | Wenhao_Jiang_Recurrent_Fusion_Network_ECCV_2018_paper.pdf | Recurrent Fusion Network for Image Captioning |
| 590 | Wenqiang_Xu_SRDA_Generating_Instance_ECCV_2018_paper.pdf | SRDA: Generating Instance SegmentationAnnotation Via Scanning, Reasoning AndDomain Adaptation |
| 591 | Wenqian_Liu_DYAN_A_Dynamical_ECCV_2018_paper.pdf | DYAN: A Dynamical Atoms-Based NetworkFor Video Prediction⋆ |
| 592 | Wonmin_Byeon_ContextVP_Fully_Context-Aware_ECCV_2018_paper.pdf | ContextVP: Fully Context-Aware VideoPrediction |
| 593 | Wonsik_Kim_Attention-based_Ensemble_for_ECCV_2018_paper.pdf | Attention-based Ensemble forDeep Metric Learning |
| 594 | Wonwoong_Cho_Text2Colors__Guiding_ECCV_2018_paper.pdf | Coloring with Words: Guiding ImageColorization Through Text-based PaletteGeneration |
| 595 | Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper.pdf | Deep Video Quality Assessor: FromSpatio-temporal Visual Sensitivity to AConvolutional Neural Aggregation Network |
| 596 | Xiangyun_Zhao_A_Modulation_Module_ECCV_2018_paper.pdf | A Modulation Module for Multi-task Learning withApplications in Image Retrieval |
| 597 | Xiangyu_He_Learning_Compression_from_ECCV_2018_paper.pdf | Learning Compression from Limited UnlabeledData |
| 598 | Xiangyu_Xu_Rendering_Portraitures_from_ECCV_2018_paper.pdf | Rendering Portraitures from Monocular Cameraand Beyond |
| 599 | Xiang_Li_Adversarial_Open-World_Person_ECCV_2018_paper.pdf | Adversarial Open-World PersonRe-Identiication |
| 600 | Xiankai_Lu_Deep_Regression_Tracking_ECCV_2018_paper.pdf | Deep Regression Tracking with Shrinkage Loss |
| 601 | Xiaodan_Liang_CIRL_Controllable_Imitative_ECCV_2018_paper.pdf | CIRL: Controllable Imitative Reinforcement Learningfor Vision-based Self-driving |
| 602 | Xiaofeng_Han_Single_Image_Water_ECCV_2018_paper.pdf | Single Image Water Hazard Detection usingFCN with Reflection Attention Units |
| 603 | Xiaofeng_Liu_Dependency-aware_Attention_Control_ECCV_2018_paper.pdf | Dependency-aware Attention Control forUnconstrained Face Recognition with Image Sets |
| 604 | Xiaohang_Zhan_Consensus-Driven_Propagation_in_ECCV_2018_paper.pdf | Consensus-Driven Propagation inMassive Unlabeled Data for Face Recognition |
| 605 | Xiaohan_Fei_Visual-Inertial_Object_Detection_ECCV_2018_paper.pdf | Visual-Inertial Ob ject Detection and Mapping |
| 606 | Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.pdf | RCAA: Relational Context-Aware Agents forPerson Search |
| 607 | Xiaokun_Wu_HandMap_Robust_Hand_ECCV_2018_paper.pdf | HandMap: Robust Hand Pose Estimation viaIntermediate Dense Guidance Map Supervision |
| 608 | Xiaolin_Zhang_Self-produced_Guidance_for_ECCV_2018_paper.pdf | Self-produced Guidance for Weakly-supervisedOb ject Localization |
| 609 | Xiaolong_Wang_Videos_as_Space-Time_ECCV_2018_paper.pdf | Videos as Space-Time Region Graphs |
| 610 | Xiaoming_Li_Learning_Warped_Guidance_ECCV_2018_paper.pdf | Learning Warped Guidance for Blind FaceRestoration |
| 611 | Xiaopeng_Zhang_ML-LocNet_Improving_Object_ECCV_2018_paper.pdf | ML-LocNet: Improving Ob ject Localization withMulti-view Learning Network |
| 612 | Xiaoqing_Ye_3D_Recurrent_Neural_ECCV_2018_paper.pdf | 3D Recurrent Neural Networks with ContextFusion for Point Cloud Semantic Segmentation |
| 613 | Xiaoqing_Yin_FishEyeRecNet_A_Multi-Context_ECCV_2018_paper.pdf | FishEyeRecNet: A Multi-Context CollaborativeDeep Network for Fisheye Image Rectification |
| 614 | Xiaoxiao_Li_Video_Object_Segmentation_ECCV_2018_paper.pdf | Video Object Segmentation with Joint Re-identificationand Attention-Aware Mask Propagation |
| 615 | Xiaoyang_Guo_Learning_Monocular_Depth_ECCV_2018_paper.pdf | Learning Monocular Depth by DistillingCross-domain Stereo Networks |
| 616 | Xiao_Sun_Integral_Human_Pose_ECCV_2018_paper.pdf | Integral Human Pose Regression |
| 617 | Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf | Recurrent Squeeze-and-Excitation ContextAggregation Net for Single Image Deraining |
| 618 | Xihui_Liu_Show_Tell_and_ECCV_2018_paper.pdf | Show, Tell and Discriminate: Image Captioningby Self-retrieval with Partially Labeled Data |
| 619 | Xinchen_Yan_Generating_Multimodal_Human_ECCV_2018_paper.pdf | MT-VAE: Learning Motion Transformationsto Generate Multimodal Human Dynamics |
| 620 | Xingang_Pan_Two_at_Once_ECCV_2018_paper.pdf | Two at Once: Enhancing Learning andGeneralization Capacities via IBN-Net |
| 621 | Xinge_Zhu_Penalizing_Top_Performers_ECCV_2018_paper.pdf | Penalizing Top Performers: Conservative Lossfor Semantic Segmentation Adaptation |
| 622 | Xingping_Dong_Triplet_Loss_with_ECCV_2018_paper.pdf | Triplet Loss in Siamese Network for Ob jectTracking |
| 623 | Xingyi_Zhou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf | Unsupervised Domain Adaptation for 3DKeypoint Estimation via View Consistency |
| 624 | Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.pdf | Grassmann Pooling as Compact HomogeneousBilinear Pooling for Fine-Grained VisualClassification |
| 625 | Xinjing_Cheng_Depth_Estimation_via_ECCV_2018_paper.pdf | Depth Estimation via Affinity Learned withConvolutional Spatial Propagation Network |
| 626 | Xinkun_Cao_Scale_Aggregation_Network_ECCV_2018_paper.pdf | Scale Aggregation Network for Accurate andEfficient Crowd Counting |
| 627 | Xinyuan_Chen_Attention-GAN_for_Object_ECCV_2018_paper.pdf | Attention-GAN for Ob ject Transfiguration inWild Images |
| 628 | Xinyu_Gong_Neural_Stereoscopic_Image_ECCV_2018_paper.pdf | Neural Stereoscopic Image Style Transfer |
| 629 | Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf | Contour Knowledge Transfer for Salient Ob jectDetection |
| 630 | Xin_Wang_Look_Before_You_ECCV_2018_paper.pdf | Look Before You Leap:Bridging Model-Free and Model-BasedReinforcement Learning for Planned-AheadVision-and-Language Navigation |
| 631 | Xin_Wang_SkipNet_Learning_Dynamic_ECCV_2018_paper.pdf | SkipNet: Learning Dynamic Routing in ConvolutionalNetworks |
| 632 | Xin_Yuan_Towards_Optimal_Deep_ECCV_2018_paper.pdf | Relaxation-Free Deep Hashing via PolicyGradient |
| 633 | Xin_Yu_Face_Super-resolution_Guided_ECCV_2018_paper.pdf | Face Super-resolution Guided byFacial Component Heatmaps |
| 634 | Xiyu_Yu_Learning_with_Biased_ECCV_2018_paper.pdf | Learning with Biased Complementary Labels |
| 635 | Xi_Zhang_Attention-aware_Deep_Adversarial_ECCV_2018_paper.pdf | Attention-aware Deep Adversarial Hashing forCross-Modal Retrieval |
| 636 | Xuanqing_Liu_Towards_Robust_Neural_ECCV_2018_paper.pdf | Towards Robust Neural Networks via RandomSelf-ensemble |
| 637 | Xuanyu_Zhu_Quaternion_Convolutional_Neural_ECCV_2018_paper.pdf | Quaternion Convolutional Neural Networks |
| 638 | Xuan_Chen_Focus_Segment_and_ECCV_2018_paper.pdf | Focus, Segment and Erase: An Efficient Networkfor Multi-Label Brain Tumor Segmentation |
| 639 | Xudong_Lin_Deep_Variational_Metric_ECCV_2018_paper.pdf | Deep Variational Metric Learning |
| 640 | Xuecheng_Nie_Mutual_Learning_to_ECCV_2018_paper.pdf | Mutual Learning to Adapt for Joint HumanParsing and Pose Estimation |
| 641 | Xuecheng_Nie_Pose_Partition_Networks_ECCV_2018_paper.pdf | Pose Partition Networks for Multi-PersonPose Estimation |
| 642 | Xuelin_Qian_Pose-Normalized_Image_Generation_ECCV_2018_paper.pdf | Pose-Normalized Image Generation for PersonRe-identification |
| 643 | Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.pdf | Multimodal UnsupervisedImage-to-Image Translation |
| 644 | XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper.pdf | A Trilateral Weighted Sparse Coding Schemefor Real-World Image Denoising |
| 645 | Xu_Lan_Person_Search_by_ECCV_2018_paper.pdf | Person Search by Multi-Scale Matching |
| 646 | Xu_Tang_PyramidBox_A_Context-assisted_ECCV_2018_paper.pdf | PyramidBox: A Context-assisted Single ShotFace Detector. |
| 647 | XU_YANG_Shuffle-Then-Assemble_Learning_Object-Agnostic_ECCV_2018_paper.pdf | Shuffle-Then-Assemble: LearningOb ject-Agnostic Visual Relationship Features |
| 648 | Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf | Fine-Grained Visual Categorization usingMeta-Learning Optimization with SampleSelection of Auxiliary Data |
| 649 | Yagiz_Aksoy_A_Dataset_of_ECCV_2018_paper.pdf | A Dataset of Flash and Ambient IlluminationPairs from the Crowd |
| 650 | Yalong_Bai_Deep_Attention_Neural_ECCV_2018_paper.pdf | Deep Attention Neural Tensor Network forVisual Question Answering |
| 651 | Yan-Pei_Cao_Learning_to_Reconstruct_ECCV_2018_paper.pdf | Learning to Reconstruct High-quality 3D Shapeswith Cascaded Fully Convolutional Networks |
| 652 | Yanbei_Chen_Semi-Supervised_Deep_Learning_ECCV_2018_paper.pdf | Semi-Supervised Deep Learning with Memory |
| 653 | Yanchao_Yang_Conditional_Prior_Networks_ECCV_2018_paper.pdf | Conditional Prior Networks for Optical Flow |
| 654 | Yandong_Li_How_Local_is_ECCV_2018_paper.pdf | How Local is the Local Diversity? ReinforcingSequential Determinantal Point Processes with DynamicGround Sets for Supervised Video Summarization |
| 655 | Yangyu_Chen_Less_is_More_ECCV_2018_paper.pdf | Less Is More: Picking Informative Frames for VideoCaptioning |
| 656 | Yang_Du_Interaction-aware_Spatio-temporal_Pyramid_ECCV_2018_paper.pdf | Interaction-aware Spatio-temporal PyramidAttention Networks for Action Classification |
| 657 | Yang_Feng_Video_Re-localization_via_ECCV_2018_paper.pdf | Video Re-localization |
| 658 | Yang_He_Diverse_Conditional_Image_ECCV_2018_paper.pdf | Diverse Conditional Image Generation byStochastic Regression withLatent Drop-Out Codes |
| 659 | Yang_Liu_Synthetically_Supervised_Feature_ECCV_2018_paper.pdf | Synthetically Supervised Feature Learning forScene Text Recognition |
| 660 | Yang_Shen_Egocentric_Activity_Prediction_ECCV_2018_paper.pdf | Egocentric Activity Prediction via EventModulated Attention |
| 661 | Yang_Shi_Question_Type_Guided_ECCV_2018_paper.pdf | Question Type Guided Attention in Visual QuestionAnswering |
| 662 | Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf | Unsupervised Domain Adaptation for SemanticSegmentation via Class-Balanced Self-Training |
| 663 | Yantao_Shen_Person_Re-identification_with_ECCV_2018_paper.pdf | Person Re-identification withDeep Similarity-Guided Graph Neural Network |
| 664 | Yanting_Pei_Does_Haze_Removal_ECCV_2018_paper.pdf | Does Haze Removal Help CNN-based ImageClassification? |
| 665 | Yan_Wang_Spatial_Pyramid_Calibration_ECCV_2018_paper.pdf | Multi-Scale Spatially-Asymmetric Recalibrationfor Image Classification |
| 666 | Yaojie_Liu_Face_De-spoofing_ECCV_2018_paper.pdf | Face De-Spoofing: Anti-Spoofing via Noise Modeling |
| 667 | Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf | Joint 3D Face Reconstruction and DenseAlignment with Position Map RegressionNetwork |
| 668 | Yao_Yao_MVSNet_Depth_Inference_ECCV_2018_paper.pdf | MVSNet: Depth Inference forUnstructured Multi-view Stereo |
| 669 | Yapeng_Tian_Audio-Visual_Event_Localization_ECCV_2018_paper.pdf | Audio-Visual Event Localization inUnconstrained Videos |
| 670 | Yasutaka_Inagaki_Learning_to_Capture_ECCV_2018_paper.pdf | Learning to Capture Light Fieldsthrough a Coded Aperture Camera |
| 671 | Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.pdf | Macro-Micro Adversarial Networkfor Human Parsing |
| 672 | yaxing_wang_Transferring_GANs_generating_ECCV_2018_paper.pdf | Transferring GANs: generating images fromlimited data |
| 673 | Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf | Deep Domain Generalization via ConditionalInvariant Adversarial Networks |
| 674 | Yedid_Hoshen_Separable_Cross-Domain_Translation_ECCV_2018_paper.pdf | NAM: Non-Adversarial Unsupervised DomainMapping |
| 675 | Yeong_Jun_Koh_Sequential_Clique_Optimization_ECCV_2018_paper.pdf | Sequential Clique Optimization for Video ObjectSegmentation |
| 676 | Ye_Yuan_3D_Ego-Pose_Estimation_ECCV_2018_paper.pdf | 3D Ego-Pose Estimation via Imitation Learning |
| 677 | Yidan_Zhou_HBE_Hand_Branch_ECCV_2018_paper.pdf | HBE: Hand Branch Ensemble Network forReal-time 3D Hand Pose Estimation |
| 678 | Yiding_Liu_Affinity_Derivation_and_ECCV_2018_paper.pdf | Affinity Derivation and Graph Merge forInstance Segmentation |
| 679 | Yifan_Sun_Beyond_Part_Models_ECCV_2018_paper.pdf | Beyond Part Models: Person Retrievalwith Refined Part Pooling(and A Strong Convolutional Baseline) |
| 680 | Yifan_Xu_SpiderCNN_Deep_Learning_ECCV_2018_paper.pdf | SpiderCNN: Deep Learning on Point Sets withParameterized Convolutional Filters |
| 681 | Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper.pdf | PlaneMatch: Patch Coplanarity Prediction forRobust RGB-D Reconstruction |
| 682 | Yihua_Cheng_Appearance-Based_Gaze_Estimation_ECCV_2018_paper.pdf | Appearance-Based Gaze Estimation viaEvaluation-Guided Asymmetric Regression |
| 683 | Yihui_He_AMC_Automated_Model_ECCV_2018_paper.pdf | AMC: AutoML for Model Compressionand Acceleration on Mobile Devices |
| 684 | Yijun_Li_A_Closed-form_Solution_ECCV_2018_paper.pdf | A Closed-form Solution toPhotorealistic Image Stylization |
| 685 | Yijun_Li_Flow-Grounded_Spatial-Temporal_Video_ECCV_2018_paper.pdf | Flow-Grounded Spatial-Temporal VideoPrediction from Still Images |
| 686 | Yikang_LI_Factorizable_Net_An_ECCV_2018_paper.pdf | Factorizable Net: An Efficient Subgraph-basedFramework for Scene Graph Generation |
| 687 | Yilei_Xiong_Move_Forward_and_ECCV_2018_paper.pdf | Move Forward and Tell: A ProgressiveGenerator of Video Descriptions |
| 688 | Yiming_Qian_Simultaneous_3D_Reconstruction_ECCV_2018_paper.pdf | Simultaneous 3D Reconstruction for WaterSurface and Underwater Scene |
| 689 | Yinda_Zhang_Active_Stereo_Net_ECCV_2018_paper.pdf | ActiveStereoNet: End-to-End Self-SupervisedLearning for Active Stereo Systems |
| 690 | Yingjie_Yao_Joint_Representation_and_ECCV_2018_paper.pdf | Joint Representation and Truncated InferenceLearning for Correlation Filter based Tracking |
| 691 | Yingwei_Li_RESOUND_Towards_Action_ECCV_2018_paper.pdf | RESOUND: Towards Action Recognitionwithout Representation Bias |
| 692 | Ying_Fu_Joint_Camera_Spectral_ECCV_2018_paper.pdf | Joint Camera Spectral Sensitivity Selection andHyperspectral Image Recovery |
| 693 | Ying_Zhang_Deep_Cross-Modal_Projection_ECCV_2018_paper.pdf | Deep Cross-Modal Pro jection Learning forImage-Text Matching |
| 694 | Yinlong_Liu_Efficient_Global_Point_ECCV_2018_paper.pdf | Efficient Global Point Cloud Registration byMatching Rotation Invariant Features ThroughTranslation Search |
| 695 | Yin_Li_In_the_Eye_ECCV_2018_paper.pdf | In the Eye of Beholder: Joint Learning of Gazeand Actions in First Person Video |
| 696 | Yin_Xia_Fictitious_GAN_Training_ECCV_2018_paper.pdf | Fictitious GAN: Training GANs with HistoricalModels |
| 697 | Yipu_Zhao_Good_Line_Cutting_ECCV_2018_paper.pdf | Good Line Cutting: towards Accurate PoseTracking of Line-assisted VO/VSLAM⋆ |
| 698 | Yiran_Zhong_Open-World_Stereo_Video_ECCV_2018_paper.pdf | Open-World Stereo Video Matchingwith Deep RNN |
| 699 | Yiran_Zhong_Stereo_Computation_for_ECCV_2018_paper.pdf | Stereo Computation for a Single Mixture Image |
| 700 | Yiru_Zhao_A_Principled_Approach_ECCV_2018_paper.pdf | An Adversarial Approach to Hard TripletGeneration |
| 701 | yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper.pdf | Orthogonal Deep Features Decomposition forAge-Invariant Face Recognition |
| 702 | Yizhen_Lao_Rolling_Shutter_Pose_ECCV_2018_paper.pdf | Rolling Shutter Pose and Ego-motion Estimationusing Shape-from-Template |
| 703 | Yi_Li_DeepIM_Deep_Iterative_ECCV_2018_paper.pdf | DeepIM: Deep Iterative Matching for 6D PoseEstimation |
| 704 | Yi_Wei_Quantization_Mimic_Towards_ECCV_2018_paper.pdf | Quantization Mimic: Towards Very Tiny CNNfor Ob ject Detection |
| 705 | Yi_Zhou_Semi-Dense_3D_Reconstruction_ECCV_2018_paper.pdf | Semi-Dense 3D Reconstruction witha Stereo Event Camera |
| 706 | Yi_Zhou_Single-view_Hair_Reconstruction_ECCV_2018_paper.pdf | HairNet: Single-View Hair Reconstruction usingConvolutional Neural Networks |
| 707 | Yongcheng_Jing_Stroke_Controllable_Fast_ECCV_2018_paper.pdf | Stroke Controllable Fast Style Transfer withAdaptive Receptive Fields⋆ |
| 708 | Yonggen_Ling_Modeling_Varying_Camera-IMU_ECCV_2018_paper.pdf | Modeling Varying Camera-IMU Time Offset inOptimization-Based Visual-Inertial Odometry |
| 709 | Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.pdf | SOD-MTGAN: Small Ob ject Detection viaMulti-Task Generative Adversarial Network |
| 710 | Yongyi_Lu_Attribute-Guided_Face_Generation_ECCV_2018_paper.pdf | Attribute-Guided Face Generation UsingConditional CycleGAN |
| 711 | Yongyi_Lu_Image_Generation_from_ECCV_2018_paper.pdf | Image Generation from Sketch Constraint UsingContextual GAN |
| 712 | Youngjae_Yu_A_Joint_Sequence_ECCV_2018_paper.pdf | A Joint Sequence Fusion Model for VideoQuestion Answering and Retrieval |
| 713 | Yu-Ting_Chen_Leveraging_Motion_Priors_ECCV_2018_paper.pdf | Leveraging Motion Priors in Videos forImproving Human Segmentation |
| 714 | Yuan-Ting_Hu_Unsupervised_Video_Object_ECCV_2018_paper.pdf | Unsupervised Video Object Segmentation using MotionSaliency-Guided Spatio-Temporal Propagation |
| 715 | Yuan-Ting_Hu_VideoMatch_Matching_based_ECCV_2018_paper.pdf | VideoMatch: Matching based Video ObjectSegmentation |
| 716 | Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper.pdf | Cross-Modal Hamming Hashing |
| 717 | Yufei_Wang_ConceptMask_Large-Scale_Segmentation_ECCV_2018_paper.pdf | Concept Mask: Large-Scale Segmentation fromSemantic Concepts |
| 718 | Yuge_Shi_Action_Anticipation_with_ECCV_2018_paper.pdf | Action Anticipation with RBF KernelizedFeature Mapping RNN |
| 719 | Yuhang_Liu_Deblurring_Natural_Image_ECCV_2018_paper.pdf | Deblurring Natural Image Using Super-Gaussian Fields |
| 720 | Yuhang_Song_Contextual_Based_Image_ECCV_2018_paper.pdf | Contextual-based Image Inpainting: Infer,Match, and Translate |
| 721 | Yujun_Cai_Weakly-supervised_3D_Hand_ECCV_2018_paper.pdf | Weakly-supervised 3D Hand Pose Estimationfrom Monocular RGB Images ⋆ |
| 722 | YuKang_Gan_Monocular_Depth_Estimation_ECCV_2018_paper.pdf | Monocular Depth Estimation with Ainity,Vertical Pooling, and Label Enhancement |
| 723 | Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper.pdf | DF-Net: Unsupervised Joint Learning ofDepth and Flow using Cross-Task Consistency |
| 724 | Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper.pdf | Image Super-Resolution Using Very DeepResidual Channel Attention Networks |
| 725 | Yumin_Suh_Part-Aligned_Bilinear_Representations_ECCV_2018_paper.pdf | Part-Aligned Bilinear Representationsfor Person Re-identification |
| 726 | Yunchao_Wei_TS2C_Tight_Box_ECCV_2018_paper.pdf | TS2C: Tight Box Mining with SurroundingSegmentation Context for Weakly SupervisedOb ject Detection |
| 727 | Yunhua_Zhang_Structured_Siamese_Network_ECCV_2018_paper.pdf | Structured Siamese Network for Real-TimeVisual Tracking |
| 728 | Yunlong_Wang_End-to-end_View_Synthesis_ECCV_2018_paper.pdf | End-to-end View Synthesis for Light FieldImaging with Pseudo 4DCNN |
| 729 | Yunpeng_Chen_Fast_Multi-fiber_Network_ECCV_2018_paper.pdf | Multi-Fiber Networks for Video Recognition |
| 730 | Yuta_Asano_Coded_Illumination_and_ECCV_2018_paper.pdf | Coded Illumination and Imaging forFluorescence Based Classification |
| 731 | Yuxin_Wu_Group_Normalization_ECCV_2018_paper.pdf | Group Normalization |
| 732 | Yu_Liu_Transductive_Centroid_Projection_ECCV_2018_paper.pdf | Transductive Centroid Pro jection forSemi-supervised Large-scale Recognition |
| 733 | zechun_liu_Bi-Real_Net_Enhancing_ECCV_2018_paper.pdf | Bi-Real Net: Enhancing the Performance of1-bit CNNs With Improved RepresentationalCapability and Advanced Training Algorithm |
| 734 | Zehao_Huang_Data-Driven_Sparse_Structure_ECCV_2018_paper.pdf | Data-Driven Sparse Structure Selection for Deep NeuralNetworks |
| 735 | Zelun_Luo_Graph_Distillation_for_ECCV_2018_paper.pdf | Graph Distillation for Action Detection withPrivileged Modalities |
| 736 | Zeming_Li_DetNet_Design_Backbone_ECCV_2018_paper.pdf | DetNet: Design Backbone for Ob ject Detection |
| 737 | Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper.pdf | Deep Volumetric Video From Very SparseMulti-View Performance Capture |
| 738 | Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.pdf | HybridFusion: Real-Time Performance CaptureUsing a Single Depth Sensor and Sparse IMUs |
| 739 | Ze_Yang_Learning_to_Navigate_ECCV_2018_paper.pdf | Learning to Navigate for Fine-grainedClassification |
| 740 | Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.pdf | Partial Adversarial Domain Adaptation |
| 741 | Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper.pdf | Learning Rigidity in Dynamic Scenes with aMoving Camera for 3D Motion Field Estimation |
| 742 | Zhaoyi_Yan_Shift-Net_Image_Inpainting_ECCV_2018_paper.pdf | Shift-Net: Image Inpainting via Deep FeatureRearrangement |
| 743 | Zhao_Chen_Estimating_Depth_from_ECCV_2018_paper.pdf | Estimating Depth from RGB and Sparse Sensing |
| 744 | Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.pdf | Towards End-to-End License Plate Detectionand Recognition: A Large Dataset and Baseline |
| 745 | Zhenfeng_Fan_Dense_Semantic_and_ECCV_2018_paper.pdf | Dense Semantic and Topological Correspondenceof 3D Faces without Landmarks |
| 746 | Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.pdf | Graph Adaptive Knowledge Transfer forUnsupervised Domain Adaptation |
| 747 | Zhengqin_Li_Materials_for_Masses_ECCV_2018_paper.pdf | Materials for Masses: SVBRDF Acquisition witha Single Mobile Phone Image |
| 748 | Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.pdf | CGIntrinsics: Better Intrinsic Image Decompositionthrough Physically-Based Rendering |
| 749 | Zheng_Dang_Eigendecomposition-free_Training_of_ECCV_2018_paper.pdf | Eigendecomposition-free Training of DeepNetworks with Zero Eigenvalue-based Losses |
| 750 | Zheng_Shou_AutoLoc_Weakly-supervised_Temporal_ECCV_2018_paper.pdf | AutoLoc: Weakly-supervised Temporal ActionLocalization in Untrimmed Videos |
| 751 | Zheng_Shou_Online_Detection_of_ECCV_2018_paper.pdf | Online Detection of Action Start in Untrimmed,Streaming Videos |
| 752 | Zheng_Zhang_Highly-Economized_Multi-View_Binary_ECCV_2018_paper.pdf | Highly-Economized Multi-View BinaryCompression for Scalable Image Clustering |
| 753 | Zheng_Zhu_Distractor-aware_Siamese_Networks_ECCV_2018_paper.pdf | Distractor-aware Siamese Networks for VisualOb ject Tracking |
| 754 | Zhenli_Zhang_ExFuse_Enhancing_Feature_ECCV_2018_paper.pdf | ExFuse: Enhancing Feature Fusion for SemanticSegmentation |
| 755 | Zhenyu_Wu_Towards_Privacy-Preserving_Visual_ECCV_2018_paper.pdf | Towards Privacy-Preserving Visual Recognitionvia Adversarial Training: A Pilot Study |
| 756 | Zhenyu_Zhang_Joint_Task-Recursive_Learning_ECCV_2018_paper.pdf | Joint Task-Recursive Learning for SemanticSegmentation and Depth Estimation |
| 757 | Zhe_Chen_Context_Refinement_for_ECCV_2018_paper.pdf | Context Refinement for Ob ject Detection |
| 758 | Zhiding_Yu_SEAL_A_Framework_ECCV_2018_paper.pdf | Simultaneous Edge Alignment and Learning |
| 759 | Zhijian_Liu_Physical_Primitive_Decomposition_ECCV_2018_paper.pdf | Physical Primitive Decomposition |
| 760 | Zhipeng_Cai_Deterministic_Consensus_Maximization_ECCV_2018_paper.pdf | Deterministic Consensus Maximization withBiconvex Programming |
| 761 | Zhiqiang_Tang_Quantized_Densely_Connected_ECCV_2018_paper.pdf | Quantized Densely Connected U-Nets forEfficient Landmark Localization |
| 762 | Zhirong_Wu_Improving_Embedding_Generalization_ECCV_2018_paper.pdf | Improving Generalization viaScalable Neighborhood Component Analysis |
| 763 | Zhiwen_Fan_A_Segmentation-aware_Deep_ECCV_2018_paper.pdf | A Segmentation-aware Deep Fusion Network forCompressed Sensing MRI |
| 764 | Zhiwen_Shao_Deep_Adaptive_Attention_ECCV_2018_paper.pdf | Deep Adaptive Attention for Joint Facial ActionUnit Detection and Face Alignment |
| 765 | Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.pdf | Deforming Autoencoders: UnsupervisedDisentangling of Shape and Appearance |
| 766 | Zhongzheng_Ren_Learning_to_Anonymize_ECCV_2018_paper.pdf | Learning to Anonymize Faces forPrivacy Preserving Action Detection |
| 767 | Zhou_GridFace_Face_Rectification_ECCV_2018_paper.pdf | GridFace: Face Rectification via Learning LocalHomography Transformations |
| 768 | Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf | Generalizing A Person Retrieval ModelHetero- and Homogeneously |
| 769 | Zihang_Meng_Efficient_Relative_Attribute_ECCV_2018_paper.pdf | Efficient Relative Attribute Learning usingGraph Neural Networks |
| 770 | Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.pdf | Saliency Detection in 360◦ Videos |
| 771 | Zixin_Luo_Learning_Local_Descriptors_ECCV_2018_paper.pdf | GeoDesc: Learning Local Descriptors byIntegrating Geometry Constraints |
| 772 | Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf | 3DFeat-Net: Weakly Supervised Local 3DFeatures for Point Cloud Registration |
| 773 | Zorah_Laehner_DeepWrinkles_Accurate_and_ECCV_2018_paper.pdf | DeepWrinkles: Accurate and Realistic ClothingModeling |
| 774 | Zuxuan_Wu_DCAN_Dual_Channel-wise_ECCV_2018_paper.pdf | DCAN: Dual Channel-wise Alignment Networksfor Unsupervised Scene Adaptation |
其中，
最佳论文奖（Best Paper Award，一篇）
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
根据 RGB 图像检测 6 维位姿的隐式三维朝向学习
论文摘要：作者们提出了一个 RGB 彩色图像处理系统，它可以进行实时的物体检测与 6 维位姿估计。其中的全新的三维朝向估计器是基于降噪自动编码的一个变种，然后借助「任务随机化」（domain randomization）技巧用 3D 模型的模拟视角进行训练。这个所谓的「增强自动编码器」相比现有的方法有数个优点：它不需要真实的、标注过位姿的训练数据，它可以泛化到多种不同的测试传单器上，并且天然地就可以处理物体和视角的对称性。这个模型学到的并不是从输入图像到物体位姿的显式映射，实际上它会根据图像样本在隐含空间内建立一个隐式的物体位姿表征。基于 T-LESS 和 LineMOD 数据集的实验表明所提的方法不仅比类似的基于模型的方法有更好的表现，而且表现也接近目前顶级的、需要真实的位姿标注图像的方法。
论文地址：
http://openaccess.thecvf.com/content_ECCV_2018/papers/Martin_Sundermeyer_Implicit_3D_Orientation_ECCV_2018_paper.pdf
最佳论文提名（Best Paper Award, Honorable Mention，两篇）
Group Normalization
组归一化
论文作者：Facebook 人工智能研究院吴育昕、何恺明。（又双叒叕是何恺明，往期最佳论文奖已经有 ICCV 2017 最佳论文以及两次 CVPR 最佳论文，可以说是最亮眼的华人研究人员之一了）
论文地址：https://arxiv.org/abs/1803.08494
GANimation: Anatomically-aware Facial Animation from a Single Image
GANimation：基于解剖学知识从单张图像生成人脸表情动画
论文摘要：生成式对抗性网络（GANs）的近期进步已经在面部表情生成任务中展现出了令人惊喜的结果。这项任务上最成功的架构是 StarGAN，它把 GANs 的图像生成过程限定在了一个具体的范围中，也就是一组不同的人做出同一个表情的照片。这种方法虽然很有效，但是它只能生成若干种离散的表情，具体是哪一种由训练数据的内容决定。为了消除这种限制，作者们在这篇论文中提出了一种新的 GAN 条件限定方式，它基于的是动作单元（Action Units）的标注，而动作单元标注就可以在一个连续的流形中描述足以定义人类表情的解剖学面部动作。通过这种方法，作者们得以控制每一个动作单元的激活程度，并且组合多个多个动作单元。除此之外，作者们还提出了一个完全无监督的策略用于训练模型，它只需要标注了激活的动作单元的图像，然后通过注意力机制的应用就可以让网络对于背景和光照条件的改变保持鲁棒。大量实验评估表明他们的方法比其他的条件生成方法有明显更好的表现，不仅表现在有能力根据解剖学上可用的肌肉动作生成非常多种多样的表情，而且也能更好地处理来自不同分布的图像。
论文地址： https://arxiv.org/abs/1807.09251
Koenderink 奖 - 时间检验奖（两篇，来自 ECCV 2008）
Hamming embedding and weak geometric consistency for large scale image search
用于大规模图像搜索的无线嵌入与弱几何一致性
Semi-supervised On-Line Boosting for Robust Tracking
用于鲁棒追踪的半监督在线增强方法.
最后
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