Method	VOC2007	VOC2010	VOC2012	ILSVRC 2013	MSCOCO 2015	Speed
OverFeat				24.3%
R-CNN (AlexNet)	58.5%	53.7%	53.3%	31.4%
R-CNN (VGG16)	66.0%
SPP_net(ZF-5)	54.2%(1-model), 60.9%(2-model)			31.84%(1-model), 35.11%(6-model)
DeepID-Net	64.1%			50.3%
NoC	73.3%		68.8%
Fast-RCNN (VGG16)	70.0%	68.8%	68.4%		19.7%(@[0.5-0.95]), 35.9%(@0.5)
MR-CNN	78.2%		73.9%
Faster-RCNN (VGG16)	78.8%		75.9%		21.9%(@[0.5-0.95]), 42.7%(@0.5)	198ms
Faster-RCNN (ResNet-101)	85.6%		83.8%		37.4%(@[0.5-0.95]), 59.0%(@0.5)
SSD300 (VGG16)	72.1%					58 fps
SSD500 (VGG16)	75.1%					23 fps
ION	79.2%		76.4%
AZ-Net	70.4%				22.3%(@[0.5-0.95]), 41.0%(@0.5)
CRAFT	75.7%		71.3%	48.5%
OHEM	78.9%		76.3%		25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCN (ResNet-50)	77.4%					0.12sec(K40), 0.09sec(TitianX)
R-FCN (ResNet-101)	79.5%					0.17sec(K40), 0.12sec(TitianX)
R-FCN (ResNet-101),multi sc train	83.6%		82.0%		31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.0	81.8%		82.5%			750ms(CPU), 46ms(TitianX)

Leaderboard

Detection Results: VOC2012

• intro: Competition “comp4” (train on own data)
• homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

Papers

Deep Neural Networks for Object Detection

• paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• intro: A deep version of the sliding window method, predicts bounding box directly from each location of the topmost feature map after knowing the confidences of the underlying object categories.
• intro: training a convolutional network to simultaneously classify, locate and detect objects in images can boost the classification accuracy and the detection and localization accuracy of all tasks
• arxiv: http://arxiv.org/abs/1312.6229
• github: https://github.com/sermanet/OverFeat
• code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv: http://arxiv.org/abs/1311.2524
• supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
• slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
• slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
• github: https://github.com/rbgirshick/rcnn
• notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
• caffe-pr(“Make R-CNN the Caffe detection example”):https://github.com/BVLC/caffe/pull/482

MultiBox

Scalable Object Detection using Deep Neural Networks

• intro: MultiBox. Train a CNN to predict Region of Interest.
• arxiv: http://arxiv.org/abs/1312.2249
• github: https://github.com/google/multibox
• blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html

Scalable, High-Quality Object Detection

• intro: MultiBox
• arxiv: http://arxiv.org/abs/1412.1441
• github: https://github.com/google/multibox

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• intro: ECCV 2014 / TPAMI 2015
• arxiv: http://arxiv.org/abs/1406.4729
• github: https://github.com/ShaoqingRen/SPP_net
• notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

• arxiv: http://arxiv.org/abs/1407.5736

DeepID-Net

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

• intro: PAMI 2016
• intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
• project page:http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
• arxiv: http://arxiv.org/abs/1412.5661

Object Detectors Emerge in Deep Scene CNNs

• arxiv: http://arxiv.org/abs/1412.6856
• paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
• paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
• slides: http://places.csail.mit.edu/slide_iclr2015.pdf

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

• intro: CVPR 2015
• project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
• arxiv: https://arxiv.org/abs/1502.04275
• github: https://github.com/YknZhu/segDeepM

NoC

Object Detection Networks on Convolutional Feature Maps

• intro: TPAMI 2015
• arxiv: http://arxiv.org/abs/1504.06066

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

• arxiv: http://arxiv.org/abs/1504.03293
• slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
• github: https://github.com/YutingZhang/fgs-obj

Fast R-CNN

Fast R-CNN

• arxiv: http://arxiv.org/abs/1504.08083
• slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
• github: https://github.com/rbgirshick/fast-rcnn
• webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
• notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
• notes: http://blog.csdn.net/linj_m/article/details/48930179
• github(“Fast R-CNN in MXNet”): https://github.com/precedenceguo/mx-rcnn
• github: https://github.com/mahyarnajibi/fast-rcnn-torch
• github: https://github.com/apple2373/chainer-simple-fast-rnn
• github(Tensorflow): https://github.com/zplizzi/tensorflow-fast-rcnn

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

• arxiv: http://arxiv.org/abs/1505.02146
• github: https://github.com/weichengkuo/DeepBox

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model

• intro: ICCV 2015. MR-CNN
• arxiv: http://arxiv.org/abs/1505.01749
• github: https://github.com/gidariss/mrcnn-object-detection
• notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
• notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
• my notes: Who can tell me why there are a bunch of duplicated sentences in section 7.2 “Detection error analysis”? :-D

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1506.01497
• gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
• slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
• github: https://github.com/ShaoqingRen/faster_rcnn
• github: https://github.com/rbgirshick/py-faster-rcnn
• github: https://github.com/mitmul/chainer-faster-rcnn
• github(Torch): https://github.com/andreaskoepf/faster-rcnn.torch
• github(Torch): https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
• github(Tensorflow): https://github.com/smallcorgi/Faster-RCNN_TF
• github(tensorflow): https://github.com/CharlesShang/TFFRCNN

Faster R-CNN in MXNet with distributed implementation and data parallelization

• github: https://github.com/dmlc/mxnet/tree/master/example/rcnn

YOLO

You Only Look Once: Unified, Real-Time Object Detection

• intro: YOLO uses the whole topmost feature map to predict both confidences for multiple categories and bounding boxes (which are shared for these categories).
• arxiv: http://arxiv.org/abs/1506.02640
• code: http://pjreddie.com/darknet/yolo/
• github: https://github.com/pjreddie/darknet
• reddit:https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
• github: https://github.com/gliese581gg/YOLO_tensorflow
• github: https://github.com/xingwangsfu/caffe-yolo
• github: https://github.com/frankzhangrui/Darknet-Yolo
• github: https://github.com/BriSkyHekun/py-darknet-yolo
• github: https://github.com/tommy-qichang/yolo.torch
• github: https://github.com/frischzenger/yolo-windows
• gtihub: https://github.com/AlexeyAB/yolo-windows

Start Training YOLO with Our Own Data

• intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
• blog: http://guanghan.info/blog/en/my-works/train-yolo/
• github: https://github.com/Guanghan/darknet

R-CNN minus R

• arxiv: http://arxiv.org/abs/1506.06981

AttentionNet

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

• intro: ICCV 2015
• intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
• arxiv: http://arxiv.org/abs/1506.07704
• slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
• slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

• arxiv: http://arxiv.org/abs/1509.04874
• demo: http://pan.baidu.com/s/1mgoWWsS
• KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php

SSD

SSD: Single Shot MultiBox Detector

• arxiv: http://arxiv.org/abs/1512.02325
• paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
• github: https://github.com/weiliu89/caffe/tree/ssd
• video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
• github(MXNet): https://github.com/zhreshold/mxnet-ssd
• github: https://github.com/zhreshold/mxnet-ssd.cpp
• github(Keras): https://github.com/rykov8/ssd_keras

为什么SSD(Single Shot MultiBox Detector)对小目标的检测效果不好？

• zhihu: https://www.zhihu.com/question/49455386

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

• intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
• arxiv: http://arxiv.org/abs/1512.04143
• slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
• coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard

Adaptive Object Detection Using Adjacency and Zoom Prediction

• intro: CVPR 2016. AZ-Net
• arxiv: http://arxiv.org/abs/1512.07711
• github: https://github.com/luyongxi/az-net
• youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM

G-CNN

G-CNN: an Iterative Grid Based Object Detector

• arxiv: http://arxiv.org/abs/1512.07729

Factors in Finetuning Deep Model for object detection Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

• intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
• project page:http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
• arxiv: http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

• arxiv: http://arxiv.org/abs/1602.08405

HyperNet

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

• arxiv: http://arxiv.org/abs/1604.00600

MultiPathNet

A MultiPath Network for Object Detection

• intro: BMVC 2016. Facebook AI Research (FAIR)
• arxiv: http://arxiv.org/abs/1604.02135
• github: https://github.com/facebookresearch/multipathnet

CRAFT

CRAFT Objects from Images

• intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
• project page: http://byangderek.github.io/projects/craft.html
• arxiv: https://arxiv.org/abs/1604.03239
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
• github: https://github.com/byangderek/CRAFT

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

• intro: CVPR 2016 Oral. Online hard example mining (OHEM)
• arxiv: http://arxiv.org/abs/1604.03540
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1604.05766

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

• arxiv: http://arxiv.org/abs/1605.06409
• github: https://github.com/daijifeng001/R-FCN
• github: https://github.com/Orpine/py-R-FCN

Weakly supervised object detection using pseudo-strong labels

• arxiv: http://arxiv.org/abs/1607.04731

Recycle deep features for better object detection

• arxiv: http://arxiv.org/abs/1607.05066

MS-CNN

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

• intro: ECCV 2016
• intro: 640×480: 15 fps, 960×720: 8 fps
• arxiv: http://arxiv.org/abs/1607.07155
• github: https://github.com/zhaoweicai/mscnn
• poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf

Multi-stage Object Detection with Group Recursive Learning

• intro: VOC2007: 78.6%, VOC2012: 74.9%
• arxiv: http://arxiv.org/abs/1608.05159

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

• intro: SubCNN
• arxiv: http://arxiv.org/abs/1604.04693
• github: https://github.com/yuxng/SubCNN

PVANET

PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

• intro: “less channels with more layers”, concatenated ReLU, Inception, and HyperNet, batch normalization, residual connections
• arxiv: http://arxiv.org/abs/1608.08021
• github: https://github.com/sanghoon/pva-faster-rcnn
• leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

• intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
• arxiv: https://arxiv.org/abs/1611.08588

GBD-Net

Gated Bi-directional CNN for Object Detection

• intro: The Chinese University of Hong Kong & Sensetime Group Limited
• paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
• mirror: https://pan.baidu.com/s/1dFohO7v

Crafting GBD-Net for Object Detection

• intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
• intro: gated bi-directional CNN (GBD-Net)
• arxiv: https://arxiv.org/abs/1610.02579
• github: https://github.com/craftGBD/craftGBD

StuffNet

StuffNet: Using ‘Stuff’ to Improve Object Detection

• arxiv: https://arxiv.org/abs/1610.05861

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

• arxiv: https://arxiv.org/abs/1610.09609

Hierarchical Object Detection with Deep Reinforcement Learning

• intro: Deep Reinforcement Learning Workshop (NIPS 2016)
• project page: https://imatge-upc.github.io/detection-2016-nipsws/
• arxiv: https://arxiv.org/abs/1611.03718
• github: https://github.com/imatge-upc/detection-2016-nipsws

Learning to detect and localize many objects from few examples

• arxiv: https://arxiv.org/abs/1611.05664

Speed/accuracy trade-offs for modern convolutional object detectors

• intro: Google Research
• arxiv: https://arxiv.org/abs/1611.10012

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

• arxiv: https://arxiv.org/abs/1612.01051

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

• intro: Facebook AI Research
• arxiv: https://arxiv.org/abs/1612.03144

Detection From Video

Learning Object Class Detectors from Weakly Annotated Video

• intro: CVPR 2012
• paper:https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf

Analysing domain shift factors between videos and images for object detection

• arxiv: https://arxiv.org/abs/1501.01186

Video Object Recognition

• slides:http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx

Deep Learning for Saliency Prediction in Natural Video

• intro: Submitted on 12 Jan 2016
• keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
• paper: https://hal.archives-ouvertes.fr/hal-01251614/document

T-CNN

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

• intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
• arxiv: http://arxiv.org/abs/1604.02532
• github: https://github.com/myfavouritekk/T-CNN

Object Detection from Video Tubelets with Convolutional Neural Networks

• intro: CVPR 2016 Spotlight paper
• arxiv: https://arxiv.org/abs/1604.04053
• paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
• gihtub: https://github.com/myfavouritekk/vdetlib

Object Detection in Videos with Tubelets and Multi-context Cues

• intro: SenseTime Group
• slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
• slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

• intro: BMVC 2016
• keywords: pseudo-labeler
• arxiv: http://arxiv.org/abs/1607.04648
• paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf

CNN Based Object Detection in Large Video Images

• intro: WangTao @ 爱奇艺
• keywords: object retrieval, object detection, scene classification
• slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf

Datasets

YouTube-Objects dataset v2.2

• homepage: http://calvin.inf.ed.ac.uk/datasets/youtube-objects-dataset/

ILSVRC2015: Object detection from video (VID)

• homepage: http://vision.cs.unc.edu/ilsvrc2015/download-videos-3j16.php#vid

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1609.06666

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

http://i.cs.hku.hk/~yzyu/vision.html

Large-scale optimization of hierarchical features for saliency prediction in natural images

• paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf

Predicting Eye Fixations using Convolutional Neural Networks

• paper: http://www.escience.cn/system/file?fileId=72648

Saliency Detection by Multi-Context Deep Learning

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

• arxiv: http://arxiv.org/abs/1510.05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

• paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

• arxiv: http://arxiv.org/abs/1603.00845
• github: https://github.com/imatge-upc/saliency-2016-cvpr

Recurrent Attentional Networks for Saliency Detection

• intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
• arxiv: http://arxiv.org/abs/1604.03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

• arxiv: http://arxiv.org/abs/1607.04730

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

• intro: CVPR 2016
• project page: http://cs-people.bu.edu/jmzhang/sod.html
• paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
• github: https://github.com/jimmie33/SOD
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

Salient Object Subitizing

• intro: CVPR 2015
• intro: predicting the existence and the number of salient objects in an image using holistic cues
• project page: http://cs-people.bu.edu/jmzhang/sos.html
• arxiv: http://arxiv.org/abs/1607.07525
• paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

• intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
• arxiv: http://arxiv.org/abs/1608.05177

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.05186

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

• arxiv: http://arxiv.org/abs/1608.08029

A Deep Multi-Level Network for Saliency Prediction

• arxiv: http://arxiv.org/abs/1609.01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1609.02077

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

• intro: DSCLRCN
• arxiv: https://arxiv.org/abs/1610.01708

Deeply supervised salient object detection with short connections

• arxiv: https://arxiv.org/abs/1611.04849

Weakly Supervised Top-down Salient Object Detection

• intro: Nanyang Technological University
• arxiv: https://arxiv.org/abs/1611.05345

Specific Object Deteciton

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

• intro: Yahoo
• arxiv: http://arxiv.org/abs/1502.02766

From Facial Parts Responses to Face Detection: A Deep Learning Approach

• project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html

Compact Convolutional Neural Network Cascade for Face Detection

• arxiv: http://arxiv.org/abs/1508.01292
• github: https://github.com/Bkmz21/FD-Evaluation

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1606.00850
• github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D

Supervised Transformer Network for Efficient Face Detection

• arxiv: http://arxiv.org/abs/1607.05477

UnitBox

UnitBox: An Advanced Object Detection Network

• intro: ACM MM 2016
• arxiv: http://arxiv.org/abs/1608.01471

Bootstrapping Face Detection with Hard Negative Examples

• author: 万韶华 @ 小米.
• intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
• arxiv: http://arxiv.org/abs/1608.02236

Grid Loss: Detecting Occluded Faces

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1609.00129
• paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
• poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

• intro: ICPR 2016
• arxiv: http://arxiv.org/abs/1609.03536

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

• project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
• arxiv: https://arxiv.org/abs/1604.02878
• github(Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
• github(MXNet): https://github.com/pangyupo/mxnet_mtcnn_face_detection
• github: https://github.com/DaFuCoding/MTCNN_Caffe

Datasets / Benchmarks

FDDB: Face Detection Data Set and Benchmark

• homepage: http://vis-www.cs.umass.edu/fddb/index.html
• results: http://vis-www.cs.umass.edu/fddb/results.html

WIDER FACE: A Face Detection Benchmark

• homepage: http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/
• arxiv: http://arxiv.org/abs/1511.06523

Facial Point / Landmark Detection

Deep Convolutional Network Cascade for Facial Point Detection

• homepage: http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
• github: https://github.com/luoyetx/deep-landmark

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1608.05477

Detecting facial landmarks in the video based on a hybrid framework

• arxiv: http://arxiv.org/abs/1609.06441

Deep Constrained Local Models for Facial Landmark Detection

• arxiv: https://arxiv.org/abs/1611.08657

People Detection

End-to-end people detection in crowded scenes

• arxiv: http://arxiv.org/abs/1506.04878
• github: https://github.com/Russell91/reinspect
• ipn:http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb

Detecting People in Artwork with CNNs

• intro: ECCV 2016 Workshops
• arxiv: https://arxiv.org/abs/1610.08871

Person Head Detection

Context-aware CNNs for person head detection

• arxiv: http://arxiv.org/abs/1511.07917
• github: https://github.com/aosokin/cnn_head_detection

Pedestrian Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

• intro: CVPR 2015
• project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
• paper: http://arxiv.org/abs/1412.0069

Deep Learning Strong Parts for Pedestrian Detection

• intro: ICCV 2015. CUHK. DeepParts
• intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

Deep convolutional neural networks for pedestrian detection

• arxiv: http://arxiv.org/abs/1510.03608
• github: https://github.com/DenisTome/DeepPed

New algorithm improves speed and accuracy of pedestrian detection

• blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php

Pushing the Limits of Deep CNNs for Pedestrian Detection

• intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
• arxiv: http://arxiv.org/abs/1603.04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

• arxiv: http://arxiv.org/abs/1607.04436

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

• arxiv: http://arxiv.org/abs/1607.04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

• arxiv: http://arxiv.org/abs/1607.07032
• github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Reduced Memory Region Based Deep Convolutional Neural Network Detection

• intro: IEEE 2016 ICCE-Berlin
• arxiv: http://arxiv.org/abs/1609.02500

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

• arxiv: https://arxiv.org/abs/1610.03466

Multispectral Deep Neural Networks for Pedestrian Detection

• intro: BMVC 2016 oral
• arxiv: https://arxiv.org/abs/1611.02644

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.04564

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

• project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
• paper: http://120.52.73.11/www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
• code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip

Boundary / Edge / Contour Detection

Holistically-Nested Edge Detection

• intro: ICCV 2015, Marr Prize
• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xie_Holistically-Nested_Edge_Detection_ICCV_2015_paper.pdf
• arxiv: http://arxiv.org/abs/1504.06375
• github: https://github.com/s9xie/hed

Unsupervised Learning of Edges

• intro: CVPR 2016. Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.04166
• zn-blog: http://www.leiphone.com/news/201607/b1trsg9j6GSMnjOP.html

Pushing the Boundaries of Boundary Detection using Deep Learning

• arxiv: http://arxiv.org/abs/1511.07386

Convolutional Oriented Boundaries

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.02755

Richer Convolutional Features for Edge Detection

• intro: richer convolutional features (RCF)
• arxiv: https://arxiv.org/abs/1612.02103

Skeleton Detection

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

• arxiv: http://arxiv.org/abs/1603.09446
• github: https://github.com/zeakey/DeepSkeleton

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

• arxiv: http://arxiv.org/abs/1609.03659

Fruit Detection

Deep Fruit Detection in Orchards

• arxiv: https://arxiv.org/abs/1610.03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

• intro: The Journal of Field Robotics in May 2016
• project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
• arxiv: https://arxiv.org/abs/1610.08120

Others

Deep Deformation Network for Object Landmark Localization

• arxiv: http://arxiv.org/abs/1605.01014

Fashion Landmark Detection in the Wild

• arxiv: http://arxiv.org/abs/1608.03049

Deep Learning for Fast and Accurate Fashion Item Detection

• intro: Kuznech Inc.
• intro: MultiBox and Fast R-CNN
• paper:https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf

Visual Relationship Detection with Language Priors

• intro: ECCV 2016 oral
• paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
• github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

• github: https://github.com/geometalab/OSMDeepOD

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

• intro: IEEE SITIS 2016
• arxiv: https://arxiv.org/abs/1611.04357

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

• arxiv: https://arxiv.org/abs/1611.05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

• intro: CMU & Magic Leap
• arxiv: https://arxiv.org/abs/1611.10010

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

• arxiv: http://arxiv.org/abs/1510.04445
• github: https://github.com/aghodrati/deepproposal

Scale-aware Pixel-wise Object Proposal Networks

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1601.04798

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

• intro: AttractioNet
• arxiv: https://arxiv.org/abs/1606.04446
• github: https://github.com/gidariss/AttractioNet

Learning to Segment Object Proposals via Recursive Neural Networks

• arxiv: https://arxiv.org/abs/1612.01057

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

• intro: PhD Thesis
• homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
• phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
• github(“SDS using hypercolumns”): https://github.com/bharath272/sds

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

• arxiv: http://arxiv.org/abs/1503.00949

Weakly Supervised Object Localization Using Size Estimates

• arxiv: http://arxiv.org/abs/1608.04314

Localizing objects using referring expressions

• intro: ECCV 2016
• keywords: LSTM, multiple instance learning (MIL)
• paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
• github: https://github.com/varun-nagaraja/referring-expressions

LocNet: Improving Localization Accuracy for Object Detection

• arxiv: http://arxiv.org/abs/1511.07763
• github: https://github.com/gidariss/LocNet

Learning Deep Features for Discriminative Localization

• homepage: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150
• github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
• github: https://github.com/metalbubble/CAM
• github: https://github.com/tdeboissiere/VGG16CAM-keras

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

• intro: ECCV 2016
• project page: http://www.di.ens.fr/willow/research/contextlocnet/
• arxiv: http://arxiv.org/abs/1609.04331
• github: https://github.com/vadimkantorov/contextlocnet

Tutorials

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

• slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Projects

TensorBox: a simple framework for training neural networks to detect objects in images

• intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
• github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

• github: https://github.com/fmassa/object-detection.torch

Using DIGITS to train an Object Detection network

• github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

FCN-MultiBox Detector

• intro: Full convolution MultiBox Detector ( like SSD) implemented in Torch.
• github: https://github.com/teaonly/FMD.torch

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

• keywords: Faster R-CNN
• blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
• demo:https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
• review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

• blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

Analyzing The Papers Behind Facebook’s Computer Vision Approach

• keywords: DeepMask, SharpMask, MultiPathNet
• blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

**Easily Create High Quality Object Detectors with Deep Learning **

• intro: dlib v19.2
• blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

• blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
• github:https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

Object Detection in Satellite Imagery, a Low Overhead Approach

• part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
• part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

• part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
• part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

• blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
• ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
• github: https://github.com/bigsnarfdude/Faster-RCNN_TF

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