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object detection资料汇总

2023-05-16
转自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

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  1. Leaderboard
  2. Papers
    1. R-CNN
    2. MultiBox
    3. SPP-Net
    4. DeepID-Net
    5. NoC
    6. Fast R-CNN
    7. DeepBox
    8. MR-CNN
    9. Faster R-CNN
    10. YOLO
    11. AttentionNet
    12. DenseBox
    13. SSD
    14. Inside-Outside Net (ION)
    15. G-CNN
    16. HyperNet
    17. MultiPathNet
    18. CRAFT
    19. OHEM
    20. R-FCN
    21. MS-CNN
    22. PVANET
    23. GBD-Net
    24. StuffNet
    25. Feature Pyramid Network (FPN)
    26. YOLOv2
    27. DSSD
    28. CC-Net
  3. Detection From Video
    1. T-CNN
    2. Datasets
  4. Object Detection in 3D
  5. Object Detection on RGB-D
  6. Salient Object Detection
    1. Saliency Detection in Video
    2. Datasets
  7. Visual Relationship Detection
  8. Specific Object Deteciton
    1. Face Deteciton
      1. UnitBox
      2. MTCNN
      3. Datasets / Benchmarks
    2. Facial Point / Landmark Detection
    3. People Detection
    4. Person Head Detection
    5. Pedestrian Detection
    6. Vehicle Detection
    7. Traffic-Sign Detection
    8. Boundary / Edge / Contour Detection
    9. Skeleton Detection
    10. Fruit Detection
    11. Part Detection
    12. Others
  9. Object Proposal
  10. Localization
  11. Tutorials / Talks
  12. Projects
  13. Blogs
MethodVOC2007VOC2010VOC2012ILSVRC 2013MSCOCO 2015Speed
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-Net64.1%  50.3%  
NoC73.3% 68.8%   
Fast-RCNN (VGG16)70.0%68.8%68.4% 19.7%(@[0.5-0.95]), 35.9%(@0.5) 
MR-CNN78.2% 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)77.2% 75.8% 25.1%(@[0.5-0.95]), 43.1%(@0.5)46 fps
SSD512 (VGG16)79.8% 78.5% 28.8%(@[0.5-0.95]), 48.5%(@0.5)19 fps
ION79.2% 76.4%   
CRAFT75.7% 71.3%48.5%  
OHEM78.9% 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 train83.6% 82.0% 31.5%(@[0.5-0.95]), 53.2%(@0.5) 
PVANet 9.089.8% 84.2%  750ms(CPU), 46ms(TitianX)

Leaderboard

Detection Results: VOC2012

  • intro: Competition “comp4” (train on additional 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

  • 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: first 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: second 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/

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
  • github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
  • 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
  • github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus

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

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

Contextual Priming and Feedback for Faster R-CNN

  • intro: ECCV 2016. Carnegie Mellon University
  • paper: http://abhinavsh.info/context_priming_feedback.pdf
  • poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf

An Implementation of Faster RCNN with Study for Region Sampling

  • intro: Technical Report, 3 pages. CMU
  • arxiv: https://arxiv.org/abs/1702.02138
  • github: https://github.com/endernewton/tf-faster-rcnn

YOLO

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

  • arxiv: http://arxiv.org/abs/1506.02640
  • code: http://pjreddie.com/darknet/yolo/
  • github: https://github.com/pjreddie/darknet
  • blog: https://pjreddie.com/publications/yolo/
  • slides:https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
  • 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

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

  • blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
  • github: https://github.com/thtrieu/darkflow

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

  • intro: ECCV 2016 Oral
  • arxiv: http://arxiv.org/abs/1512.02325
  • paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
  • slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.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
  • github(TensorFlow): https://github.com/balancap/SSD-Tensorflow
  • github: https://github.com/amdegroot/ssd.pytorch

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
  • github（Official）: https://github.com/abhi2610/ohem
  • author page: http://abhinav-shrivastava.info/

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

  • intro: scale-dependent pooling (SDP), cascaded rejection clas-sifiers (CRC)
  • paper: 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
  • github(PyTorch): https://github.com/PureDiors/pytorch_RFCN
  • github: https://github.com/bharatsingh430/py-R-FCN-multiGPU

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: WACV 2017. 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
  • slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
  • github: https://github.com/imatge-upc/detection-2016-nipsws
  • blog: http://jorditorres.org/nips/

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
  • github: https://github.com/BichenWuUCB/squeezeDet

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

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

Action-Driven Object Detection with Top-Down Visual Attentions

  • arxiv: https://arxiv.org/abs/1612.06704

Beyond Skip Connections: Top-Down Modulation for Object Detection

  • intro: CMU & UC Berkeley & Google Research
  • arxiv: https://arxiv.org/abs/1612.06851

YOLOv2

YOLO9000: Better, Faster, Stronger

  • arxiv: https://arxiv.org/abs/1612.08242
  • code: http://pjreddie.com/yolo9000/
  • github(Chainer): https://github.com/leetenki/YOLOv2
  • github(Keras): https://github.com/allanzelener/YAD2K
  • github(PyTorch): https://github.com/longcw/yolo2-pytorch
  • github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
  • github(Windows): https://github.com/AlexeyAB/darknet
  • github: https://github.com/choasUp/caffe-yolo9000

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

  • github: https://github.com/AlexeyAB/Yolo_mark

DSSD

DSSD : Deconvolutional Single Shot Detector

  • intro: UNC Chapel Hill & Amazon Inc
  • arxiv: https://arxiv.org/abs/1701.06659

Wide-Residual-Inception Networks for Real-time Object Detection

  • intro: Inha University
  • arxiv: https://arxiv.org/abs/1702.01243

Attentional Network for Visual Object Detection

  • intro: University of Maryland & Mitsubishi Electric Research Laboratories
  • arxiv: https://arxiv.org/abs/1702.01478

CC-Net

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

  • intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
  • arxiv: https://arxiv.org/abs/1702.07054

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

https://arxiv.org/abs/1703.10295

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

  • intro: CVPR 2017
  • paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
  • github(Caffe): https://github.com/xiaolonw/adversarial-frcnn

Spatial Memory for Context Reasoning in Object Detection

  • arxiv: https://arxiv.org/abs/1704.04224

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

Object Detection in Videos with Tubelet Proposal Networks

  • arxiv: https://arxiv.org/abs/1702.06355

Flow-Guided Feature Aggregation for Video Object Detection

  • intro: MSRA
  • arxiv: https://arxiv.org/abs/1703.10025

Video Object Detection using Faster R-CNN

  • blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
  • github: https://github.com/andrewliao11/py-faster-rcnn-imagenet

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

Object Detection on RGB-D

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

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

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

  • intro: CVPR 2016
  • paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

https://arxiv.org/abs/1703.03347

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

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

  • project page: https://imatge-upc.github.io/saliency-salgan-2017/
  • arxiv: https://arxiv.org/abs/1701.01081

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

  • arxiv: https://arxiv.org/abs/1702.00372

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

  • arxiv: https://arxiv.org/abs/1702.00615

Saliency Detection by Forward and Backward Cues in Deep-CNNs

https://arxiv.org/abs/1703.00152

Saliency Detection in Video

Deep Learning For Video Saliency Detection

  • arxiv: https://arxiv.org/abs/1702.00871

Datasets

MSRA10K Salient Object Database

http://mmcheng.net/msra10k/

Visual Relationship Detection

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

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

  • intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
  • arxiv: https://arxiv.org/abs/1702.07191

Visual Translation Embedding Network for Visual Relation Detection

  • arxiv: https://www.arxiv.org/abs/1702.08319

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

  • intro: CVPR 2017 spotlight paper
  • arxiv: https://arxiv.org/abs/1703.03054

Specific Object Deteciton

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

  • intro: Yahoo
  • arxiv: http://arxiv.org/abs/1502.02766
  • github: https://github.com/guoyilin/FaceDetection_CNN

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

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

  • intro: CMU
  • arxiv: https://arxiv.org/abs/1606.05413

Finding Tiny Faces

  • intro: CMU
  • project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
  • arxiv: https://arxiv.org/abs/1612.04402
  • github: https://github.com/peiyunh/tiny

Towards a Deep Learning Framework for Unconstrained Face Detection

  • intro: overlap with CMS-RCNN
  • arxiv: https://arxiv.org/abs/1612.05322

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: https://github.com/pangyupo/mxnet_mtcnn_face_detection
  • github: https://github.com/DaFuCoding/MTCNN_Caffe
  • github(MXNet): https://github.com/Seanlinx/mtcnn
  • github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
  • github(Caffe): https://github.com/foreverYoungGitHub/MTCNN

Face Detection using Deep Learning: An Improved Faster RCNN Approach

  • intro: DeepIR Inc
  • arxiv: https://arxiv.org/abs/1701.08289

Faceness-Net: Face Detection through Deep Facial Part Responses

  • intro: An extended version of ICCV 2015 paper
  • arxiv: https://arxiv.org/abs/1701.08393

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

  • intro: CVPR 2017. MP-RCNN, MP-RPN
  • arxiv: https://arxiv.org/abs/1703.09145

End-To-End Face Detection and Recognition

https://arxiv.org/abs/1703.10818

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

Facial Landmark Detection by Deep Multi-task Learning

  • intro: ECCV 2014
  • project page: http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html
  • paper: http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf
  • github(Matlab): https://github.com/zhzhanp/TCDCN-face-alignment

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

Effective face landmark localization via single deep network

  • arxiv: https://arxiv.org/abs/1702.02719

A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

https://arxiv.org/abs/1704.01880

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

Deep Multi-camera People Detection

  • arxiv: https://arxiv.org/abs/1702.04593

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

Scale-aware Fast R-CNN for Pedestrian Detection

  • arxiv: https://arxiv.org/abs/1510.08160

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?

  • intro: ECCV 2016
  • 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

Evolving Boxes for fast Vehicle Detection

  • arxiv: https://arxiv.org/abs/1702.00254

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

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

  • project page: http://www.vision.ee.ethz.ch/~cvlsegmentation/
  • arxiv: https://arxiv.org/abs/1701.04658
  • github: https://github.com/kmaninis/COB

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

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

  • intro: CVPR 2017
  • arxiv: https://arxiv.org/abs/1703.02243
  • github: https://github.com/KevinKecc/SRN

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

Part Detection

Objects as context for part detection

https://arxiv.org/abs/1703.09529

Others

Deep Deformation Network for Object Landmark Localization

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

Fashion Landmark Detection in the Wild

  • intro: ECCV 2016
  • project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
  • arxiv: http://arxiv.org/abs/1608.03049
  • github(Caffe): https://github.com/liuziwei7/fashion-landmarks

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

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

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

  • arxiv: https://arxiv.org/abs/1612.03019

Deep Learning Logo Detection with Data Expansion by Synthesising Context

  • arxiv: https://arxiv.org/abs/1612.09322

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

  • arxiv: https://arxiv.org/abs/1702.00307

Automatic Handgun Detection Alarm in Videos Using Deep Learning

  • arxiv: https://arxiv.org/abs/1702.05147
  • results: https://github.com/SihamTabik/Pistol-Detection-in-Videos

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: BMVC 2016. 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

Learning Detection with Diverse Proposals

  • intro: CVPR 2017
  • keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
  • arxiv: https://arxiv.org/abs/1704.03533

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

Active Object Localization with Deep Reinforcement Learning

  • intro: ICCV 2015
  • keywords: Markov Decision Process
  • arxiv: https://arxiv.org/abs/1511.06015

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 / Talks

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

Towards Good Practices for Recognition & Detection

  • intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
  • slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.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

KittiBox: A car detection model implemented in Tensorflow.

  • keywords: MultiNet
  • intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
  • github: https://github.com/MarvinTeichmann/KittiBox

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

Small U-Net for vehicle detection

  • blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad

« NLP



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