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Detection Results: VOC2012
- intro: Competition “comp4” (train on own data)
- homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid114
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?idsoftware: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(codedata): 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 114
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 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 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(codedataset): 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 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