目标检测最新方法介绍

快来打我* 2022-07-13 08:15 251阅读 0赞

https://handong1587.github.io/deep\_learning/2015/10/09/nlp.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)][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)][Feature Pyramid Network _FPN]
3.  [Detection From Video][]
    
    1.  [T-CNN][]
    2.  [Datasets][]
4.  [Object Detection in 3D][]
5.  [Salient Object Detection][]
6.  [Specific Object Deteciton][]
    
    1.  [Face Deteciton][]
        
        1.  [UnitBox][]
        2.  [MTCNN][]
        3.  [Datasets / Benchmarks][Datasets _ Benchmarks]
    2.  [Facial Point / Landmark Detection][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][Boundary _ Edge _ Contour Detection]
    9.  [Skeleton Detection][]
    10. [Fruit Detection][]
    11. [Others][]
7.  [Object Proposal][]
8.  [Localization][]
9.  [Tutorials][]
10. [Projects][]
11. [Blogs][]

<table style="font-family:Arial; font-size:14px; margin:0px 0px 1em; padding:0px; position:relative; width:600px; color:rgb(77,77,77)"> 
 <thead style="margin:0px; padding:0px"> 
  <tr style="margin:0px; padding:0px"> 
   <th style="margin:0px; padding:4px 0px; text-align:center">Method</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">VOC2007</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">VOC2010</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">VOC2012</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">ILSVRC 2013</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">MSCOCO 2015</th> 
   <th style="margin:0px; padding:4px 0px; text-align:center">Speed</th> 
  </tr> 
 </thead> 
 <tbody style="margin:0px; padding:0px"> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">OverFeat</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">24.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">R-CNN (AlexNet)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">58.5%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">53.7%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">53.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">31.4%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">R-CNN (VGG16)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">66.0%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">SPP_net(ZF-5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">54.2%(1-model), 60.9%(2-model)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">31.84%(1-model), 35.11%(6-model)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">DeepID-Net</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">64.1%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">50.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">NoC</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">73.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">68.8%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">Fast-RCNN (VGG16)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">70.0%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">68.8%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">68.4%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">19.7%(@[0.5-0.95]), 35.9%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">MR-CNN</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">78.2%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">73.9%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">Faster-RCNN (VGG16)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">78.8%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">75.9%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">21.9%(@[0.5-0.95]), 42.7%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">198ms</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">Faster-RCNN (ResNet-101)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">85.6%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">83.8%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">37.4%(@[0.5-0.95]), 59.0%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">SSD300 (VGG16)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">72.1%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">58 fps</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">SSD500 (VGG16)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">75.1%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">23 fps</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">ION</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">79.2%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">76.4%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">AZ-Net</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">70.4%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">22.3%(@[0.5-0.95]), 41.0%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">CRAFT</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">75.7%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">71.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">48.5%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">OHEM</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">78.9%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">76.3%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">25.5%(@[0.5-0.95]), 45.9%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">R-FCN (ResNet-50)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">77.4%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">0.12sec(K40), 0.09sec(TitianX)</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">R-FCN (ResNet-101)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">79.5%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">0.17sec(K40), 0.12sec(TitianX)</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">R-FCN (ResNet-101),multi sc train</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">83.6%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">82.0%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">31.5%(@[0.5-0.95]), 53.2%(@0.5)</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
  </tr> 
  <tr style="margin:0px; padding:0px"> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">PVANet 9.0</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">81.8%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">82.5%</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">&nbsp;</td> 
   <td style="margin:0px; padding:4px 0px; text-indent:6px; text-align:center">750ms(CPU), 46ms(TitianX)</td> 
  </tr> 
 </tbody> 
</table>

# 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][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][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][http_arxiv.org_abs_1312.6229]
 *  github: [https://github.com/sermanet/OverFeat][https_github.com_sermanet_OverFeat]
 *  code: [http://cilvr.nyu.edu/doku.php?id=software:overfeat:start][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][http_arxiv.org_abs_1311.2524]
 *  supp: [http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf][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][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][http_www.cs.berkeley.edu_rbg_slides_rcnn-cvpr14-slides.pdf]
 *  github: [https://github.com/rbgirshick/rcnn][https_github.com_rbgirshick_rcnn]
 *  notes: [http://zhangliliang.com/2014/07/23/paper-note-rcnn/][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][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][http_arxiv.org_abs_1312.2249]
 *  github: [https://github.com/google/multibox][https_github.com_google_multibox]
 *  blog: [https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html][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][http_arxiv.org_abs_1412.1441]
 *  github: [https://github.com/google/multibox][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][http_arxiv.org_abs_1406.4729]
 *  github: [https://github.com/ShaoqingRen/SPP\_net][https_github.com_ShaoqingRen_SPP_net]
 *  notes: [http://zhangliliang.com/2014/09/13/paper-note-sppnet/][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][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][http_www.ee.cuhk.edu.hk_CB_9Cwlouyang_projects_imagenetDeepId_index.html]
 *  arxiv: [http://arxiv.org/abs/1412.5661][http_arxiv.org_abs_1412.5661]

Object Detectors Emerge in Deep Scene CNNs

*  arxiv: [http://arxiv.org/abs/1412.6856][http_arxiv.org_abs_1412.6856]
 *  paper: [https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou\_iclr15.pdf][https_www.robots.ox.ac.uk_vgg_rg_papers_zhou_iclr15.pdf]
 *  paper: [https://people.csail.mit.edu/khosla/papers/iclr2015\_zhou.pdf][https_people.csail.mit.edu_khosla_papers_iclr2015_zhou.pdf]
 *  slides: [http://places.csail.mit.edu/slide\_iclr2015.pdf][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][https_www.cs.toronto.edu_yukun_segdeepm.html]
 *  arxiv: [https://arxiv.org/abs/1502.04275][https_arxiv.org_abs_1502.04275]
 *  github: [https://github.com/YknZhu/segDeepM][https_github.com_YknZhu_segDeepM]

## NoC ##

Object Detection Networks on Convolutional Feature Maps

*  intro: TPAMI 2015
 *  arxiv: [http://arxiv.org/abs/1504.06066][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][http_arxiv.org_abs_1504.03293]
 *  slides: [http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf][http_www.ytzhang.net_files_publications_2015-cvpr-det-slides.pdf]
 *  github: [https://github.com/YutingZhang/fgs-obj][https_github.com_YutingZhang_fgs-obj]

## Fast R-CNN ##

Fast R-CNN

*  arxiv: [http://arxiv.org/abs/1504.08083][http_arxiv.org_abs_1504.08083]
 *  slides: [http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf][http_tutorial.caffe.berkeleyvision.org_caffe-cvpr15-detection.pdf]
 *  github: [https://github.com/rbgirshick/fast-rcnn][https_github.com_rbgirshick_fast-rcnn]
 *  webcam demo: [https://github.com/rbgirshick/fast-rcnn/pull/29][https_github.com_rbgirshick_fast-rcnn_pull_29]
 *  notes: [http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/][http_zhangliliang.com_2015_05_17_paper-note-fast-rcnn]
 *  notes: [http://blog.csdn.net/linj\_m/article/details/48930179][http_blog.csdn.net_linj_m_article_details_48930179]
 *  github(“Fast R-CNN in MXNet”): [https://github.com/precedenceguo/mx-rcnn][https_github.com_precedenceguo_mx-rcnn]
 *  github: [https://github.com/mahyarnajibi/fast-rcnn-torch][https_github.com_mahyarnajibi_fast-rcnn-torch]
 *  github: [https://github.com/apple2373/chainer-simple-fast-rnn][https_github.com_apple2373_chainer-simple-fast-rnn]
 *  github(Tensorflow): [https://github.com/zplizzi/tensorflow-fast-rcnn][https_github.com_zplizzi_tensorflow-fast-rcnn]

## DeepBox ##

DeepBox: Learning Objectness with Convolutional Networks

*  arxiv: [http://arxiv.org/abs/1505.02146][http_arxiv.org_abs_1505.02146]
 *  github: [https://github.com/weichengkuo/DeepBox][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][http_arxiv.org_abs_1505.01749]
 *  github: [https://github.com/gidariss/mrcnn-object-detection][https_github.com_gidariss_mrcnn-object-detection]
 *  notes: [http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/][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/][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][http_arxiv.org_abs_1506.01497]
 *  gitxiv: [http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region][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][http_web.cs.hacettepe.edu.tr_aykut_classes_spring2016_bil722_slides_w05-FasterR-CNN.pdf]
 *  github: [https://github.com/ShaoqingRen/faster\_rcnn][https_github.com_ShaoqingRen_faster_rcnn]
 *  github: [https://github.com/rbgirshick/py-faster-rcnn][https_github.com_rbgirshick_py-faster-rcnn]
 *  github: [https://github.com/mitmul/chainer-faster-rcnn][https_github.com_mitmul_chainer-faster-rcnn]
 *  github(Torch): [https://github.com/andreaskoepf/faster-rcnn.torch][https_github.com_andreaskoepf_faster-rcnn.torch]
 *  github(Torch): [https://github.com/ruotianluo/Faster-RCNN-Densecap-torch][https_github.com_ruotianluo_Faster-RCNN-Densecap-torch]
 *  github(Tensorflow): [https://github.com/smallcorgi/Faster-RCNN\_TF][https_github.com_smallcorgi_Faster-RCNN_TF]
 *  github(tensorflow): [https://github.com/CharlesShang/TFFRCNN][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][https_github.com_dmlc_mxnet_tree_master_example_rcnn]

## YOLO ##

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

![687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67][]

*  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][http_arxiv.org_abs_1506.02640]
 *  code: [http://pjreddie.com/darknet/yolo/][http_pjreddie.com_darknet_yolo]
 *  github: [https://github.com/pjreddie/darknet][https_github.com_pjreddie_darknet]
 *  reddit:[https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime\_object\_detection\_with\_yolo/][https_www.reddit.com_r_MachineLearning_comments_3a3m0o_realtime_object_detection_with_yolo]
 *  github: [https://github.com/gliese581gg/YOLO\_tensorflow][https_github.com_gliese581gg_YOLO_tensorflow]
 *  github: [https://github.com/xingwangsfu/caffe-yolo][https_github.com_xingwangsfu_caffe-yolo]
 *  github: [https://github.com/frankzhangrui/Darknet-Yolo][https_github.com_frankzhangrui_Darknet-Yolo]
 *  github: [https://github.com/BriSkyHekun/py-darknet-yolo][https_github.com_BriSkyHekun_py-darknet-yolo]
 *  github: [https://github.com/tommy-qichang/yolo.torch][https_github.com_tommy-qichang_yolo.torch]
 *  github: [https://github.com/frischzenger/yolo-windows][https_github.com_frischzenger_yolo-windows]
 *  gtihub: [https://github.com/AlexeyAB/yolo-windows][https_github.com_AlexeyAB_yolo-windows]

Start Training YOLO with Our Own Data

![images-40.jpg][]

*  intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
 *  blog: [http://guanghan.info/blog/en/my-works/train-yolo/][http_guanghan.info_blog_en_my-works_train-yolo]
 *  github: [https://github.com/Guanghan/darknet][https_github.com_Guanghan_darknet]

R-CNN minus R

*  arxiv: [http://arxiv.org/abs/1506.06981][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][http_arxiv.org_abs_1506.07704]
 *  slides: [https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf][https_www.robots.ox.ac.uk_vgg_rg_slides_AttentionNet.pdf]
 *  slides: [http://image-net.org/challenges/talks/lunit-kaist-slide.pdf][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][http_arxiv.org_abs_1509.04874]
 *  demo: [http://pan.baidu.com/s/1mgoWWsS][http_pan.baidu.com_s_1mgoWWsS]
 *  KITTI result: [http://www.cvlibs.net/datasets/kitti/eval\_object.php][http_www.cvlibs.net_datasets_kitti_eval_object.php]

## SSD ##

SSD: Single Shot MultiBox Detector

![687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67][]

*  arxiv: [http://arxiv.org/abs/1512.02325][http_arxiv.org_abs_1512.02325]
 *  paper: [http://www.cs.unc.edu/~wliu/papers/ssd.pdf][http_www.cs.unc.edu_wliu_papers_ssd.pdf]
 *  github: [https://github.com/weiliu89/caffe/tree/ssd][https_github.com_weiliu89_caffe_tree_ssd]
 *  video: [http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973][http_weibo.com_p_2304447a2326da963254c963c97fb05dd3a973]
 *  github(MXNet): [https://github.com/zhreshold/mxnet-ssd][https_github.com_zhreshold_mxnet-ssd]
 *  github: [https://github.com/zhreshold/mxnet-ssd.cpp][https_github.com_zhreshold_mxnet-ssd.cpp]
 *  github(Keras): [https://github.com/rykov8/ssd\_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][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][http_arxiv.org_abs_1512.04143]
 *  slides: [http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf][http_www.seanbell.ca_tmp_ion-coco-talk-bell2015.pdf]
 *  coco-leaderboard: [http://mscoco.org/dataset/\#detections-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][http_arxiv.org_abs_1512.07711]
 *  github: [https://github.com/luyongxi/az-net][https_github.com_luyongxi_az-net]
 *  youtube: [https://www.youtube.com/watch?v=YmFtuNwxaNM][https_www.youtube.com_watch_v_YmFtuNwxaNM]

## G-CNN ##

G-CNN: an Iterative Grid Based Object Detector

*  arxiv: [http://arxiv.org/abs/1512.07729][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][http_www.ee.cuhk.edu.hk_wlouyang_projects_ImageNetFactors_CVPR16.html]
 *  arxiv: [http://arxiv.org/abs/1601.05150][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][http_arxiv.org_abs_1602.08405]

## HyperNet ##

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

*  arxiv: [http://arxiv.org/abs/1604.00600][http_arxiv.org_abs_1604.00600]

## MultiPathNet ##

A MultiPath Network for Object Detection

![442d027a-666e-11e6-9a1e-2fac95a2d3ba.jpg][]

*  intro: BMVC 2016. Facebook AI Research (FAIR)
 *  arxiv: [http://arxiv.org/abs/1604.02135][http_arxiv.org_abs_1604.02135]
 *  github: [https://github.com/facebookresearch/multipathnet][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][http_byangderek.github.io_projects_craft.html]
 *  arxiv: [https://arxiv.org/abs/1604.03239][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][http_www.cv-foundation.org_openaccess_content_cvpr_2016_papers_Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf]
 *  github: [https://github.com/byangderek/CRAFT][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][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][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][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][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][http_arxiv.org_abs_1605.06409]
 *  github: [https://github.com/daijifeng001/R-FCN][https_github.com_daijifeng001_R-FCN]
 *  github: [https://github.com/Orpine/py-R-FCN][https_github.com_Orpine_py-R-FCN]

Weakly supervised object detection using pseudo-strong labels

*  arxiv: [http://arxiv.org/abs/1607.04731][http_arxiv.org_abs_1607.04731]

Recycle deep features for better object detection

*  arxiv: [http://arxiv.org/abs/1607.05066][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][http_arxiv.org_abs_1607.07155]
 *  github: [https://github.com/zhaoweicai/mscnn][https_github.com_zhaoweicai_mscnn]
 *  poster: [http://www.eccv2016.org/files/posters/P-2B-38.pdf][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][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][http_arxiv.org_abs_1604.04693]
 *  github: [https://github.com/yuxng/SubCNN][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][http_arxiv.org_abs_1608.08021]
 *  github: [https://github.com/sanghoon/pva-faster-rcnn][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][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_1608.08021]
 *  arxiv: [https://arxiv.org/abs/1611.08588][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][http_link.springer.com_chapter_10.1007_978-3-319-46478-7_22]
 *  mirror: [https://pan.baidu.com/s/1dFohO7v][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][https_arxiv.org_abs_1610.02579]
 *  github: [https://github.com/craftGBD/craftGBD][https_github.com_craftGBD_craftGBD]

## StuffNet ##

StuffNet: Using ‘Stuff’ to Improve Object Detection

*  arxiv: [https://arxiv.org/abs/1610.05861][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][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/][https_imatge-upc.github.io_detection-2016-nipsws]
 *  arxiv: [https://arxiv.org/abs/1611.03718][https_arxiv.org_abs_1611.03718]
 *  github: [https://github.com/imatge-upc/detection-2016-nipsws][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][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][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][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][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][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][https_arxiv.org_abs_1501.01186]

Video Object Recognition

*  slides:[http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx][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][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][http_arxiv.org_abs_1604.02532]
 *  github: [https://github.com/myfavouritekk/T-CNN][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][https_arxiv.org_abs_1604.04053]
 *  paper: [http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet\_CVPR16.pdf][http_www.ee.cuhk.edu.hk_wlouyang_Papers_KangVideoDet_CVPR16.pdf]
 *  gihtub: [https://github.com/myfavouritekk/vdetlib][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][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][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][http_arxiv.org_abs_1607.04648]
 *  paper: [http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video\_object\_detection\_BMVC.pdf][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][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/][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][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][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][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][http_coxlab.org_pdfs_cvpr2014_vig_saliency.pdf]

Predicting Eye Fixations using Convolutional Neural Networks

*  paper: [http://www.escience.cn/system/file?fileId=72648][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][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][http_arxiv.org_abs_1510.05484]

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

![445461979.png][]

*  paper: [www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html][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][http_arxiv.org_abs_1603.00845]
 *  github: [https://github.com/imatge-upc/saliency-2016-cvpr][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][http_arxiv.org_abs_1604.03227]

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

*  arxiv: [http://arxiv.org/abs/1607.04730][http_arxiv.org_abs_1607.04730]

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

![pasted_20image_201465x373.jpg][]

*  intro: CVPR 2016
 *  project page: [http://cs-people.bu.edu/jmzhang/sod.html][http_cs-people.bu.edu_jmzhang_sod.html]
 *  paper: [http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD\_camera\_ready.pdf][http_cs-people.bu.edu_jmzhang_SOD_CVPR16SOD_camera_ready.pdf]
 *  github: [https://github.com/jimmie33/SOD][https_github.com_jimmie33_SOD]
 *  caffe model zoo: [https://github.com/BVLC/caffe/wiki/Model-Zoo\#cnn-object-proposal-models-for-salient-object-detection][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][http_www.cv-foundation.org_openaccess_content_cvpr_2016_papers_Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf]

Salient Object Subitizing

![frontpage.png_crc_123070793][]

*  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][http_cs-people.bu.edu_jmzhang_sos.html]
 *  arxiv: [http://arxiv.org/abs/1607.07525][http_arxiv.org_abs_1607.07525]
 *  paper: [http://cs-people.bu.edu/jmzhang/SOS/SOS\_preprint.pdf][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][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][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][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][http_arxiv.org_abs_1608.08029]

A Deep Multi-Level Network for Saliency Prediction

*  arxiv: [http://arxiv.org/abs/1609.01064][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][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][https_arxiv.org_abs_1610.01708]

Deeply supervised salient object detection with short connections

*  arxiv: [https://arxiv.org/abs/1611.04849][https_arxiv.org_abs_1611.04849]

Weakly Supervised Top-down Salient Object Detection

*  intro: Nanyang Technological University
 *  arxiv: [https://arxiv.org/abs/1611.05345][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][http_arxiv.org_abs_1502.02766]

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

![index.png][]

*  project page: [http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html][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][http_arxiv.org_abs_1508.01292]
 *  github: [https://github.com/Bkmz21/FD-Evaluation][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][https_arxiv.org_abs_1606.00850]
 *  github(MXNet): [https://github.com/tfwu/FaceDetection-ConvNet-3D][https_github.com_tfwu_FaceDetection-ConvNet-3D]

Supervised Transformer Network for Efficient Face Detection

*  arxiv: [http://arxiv.org/abs/1607.05477][http_arxiv.org_abs_1607.05477]

### UnitBox ###

UnitBox: An Advanced Object Detection Network

*  intro: ACM MM 2016
 *  arxiv: [http://arxiv.org/abs/1608.01471][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][http_arxiv.org_abs_1608.02236]

Grid Loss: Detecting Occluded Faces

*  intro: ECCV 2016
 *  arxiv: [https://arxiv.org/abs/1609.00129][https_arxiv.org_abs_1609.00129]
 *  paper: [http://lrs.icg.tugraz.at/pubs/opitz\_eccv\_16.pdf][http_lrs.icg.tugraz.at_pubs_opitz_eccv_16.pdf]
 *  poster: [http://www.eccv2016.org/files/posters/P-2A-34.pdf][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][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

![index.png 1][]

*  project page: [https://kpzhang93.github.io/MTCNN\_face\_detection\_alignment/index.html][https_kpzhang93.github.io_MTCNN_face_detection_alignment_index.html]
 *  arxiv: [https://arxiv.org/abs/1604.02878][https_arxiv.org_abs_1604.02878]
 *  github(Matlab): [https://github.com/kpzhang93/MTCNN\_face\_detection\_alignment][https_github.com_kpzhang93_MTCNN_face_detection_alignment]
 *  github(MXNet): [https://github.com/pangyupo/mxnet\_mtcnn\_face\_detection][https_github.com_pangyupo_mxnet_mtcnn_face_detection]
 *  github: [https://github.com/DaFuCoding/MTCNN\_Caffe][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][http_vis-www.cs.umass.edu_fddb_index.html]
 *  results: [http://vis-www.cs.umass.edu/fddb/results.html][http_vis-www.cs.umass.edu_fddb_results.html]

WIDER FACE: A Face Detection Benchmark

![intro.jpg][]

*  homepage: [http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/][http_mmlab.ie.cuhk.edu.hk_projects_WIDERFace]
 *  arxiv: [http://arxiv.org/abs/1511.06523][http_arxiv.org_abs_1511.06523]

## Facial Point / Landmark Detection ##

Deep Convolutional Network Cascade for Facial Point Detection

![Picture1.png][]

*  homepage: [http://mmlab.ie.cuhk.edu.hk/archive/CNN\_FacePoint.htm][http_mmlab.ie.cuhk.edu.hk_archive_CNN_FacePoint.htm]
 *  paper: [http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf][http_www.ee.cuhk.edu.hk_xgwang_papers_sunWTcvpr13.pdf]
 *  github: [https://github.com/luoyetx/deep-landmark][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][https_arxiv.org_abs_1608.05477]

Detecting facial landmarks in the video based on a hybrid framework

*  arxiv: [http://arxiv.org/abs/1609.06441][http_arxiv.org_abs_1609.06441]

Deep Constrained Local Models for Facial Landmark Detection

*  arxiv: [https://arxiv.org/abs/1611.08657][https_arxiv.org_abs_1611.08657]

## People Detection ##

End-to-end people detection in crowded scenes

![end_to_end_people_detection_in_crowded_scenes.jpg][]

*  arxiv: [http://arxiv.org/abs/1506.04878][http_arxiv.org_abs_1506.04878]
 *  github: [https://github.com/Russell91/reinspect][https_github.com_Russell91_reinspect]
 *  ipn:[http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation\_reinspect.ipynb][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][https_arxiv.org_abs_1610.08871]

## Person Head Detection ##

Context-aware CNNs for person head detection

*  arxiv: [http://arxiv.org/abs/1511.07917][http_arxiv.org_abs_1511.07917]
 *  github: [https://github.com/aosokin/cnn\_head\_detection][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/][http_mmlab.ie.cuhk.edu.hk_projects_TA-CNN]
 *  paper: [http://arxiv.org/abs/1412.0069][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][http_personal.ie.cuhk.edu.hk_pluo_pdf_tianLWTiccv15.pdf]

Deep convolutional neural networks for pedestrian detection

*  arxiv: [http://arxiv.org/abs/1510.03608][http_arxiv.org_abs_1510.03608]
 *  github: [https://github.com/DenisTome/DeepPed][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][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][http_arxiv.org_abs_1603.04525]

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

*  arxiv: [http://arxiv.org/abs/1607.04436][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][http_arxiv.org_abs_1607.04441]

Is Faster R-CNN Doing Well for Pedestrian Detection?

*  arxiv: [http://arxiv.org/abs/1607.07032][http_arxiv.org_abs_1607.07032]
 *  github: [https://github.com/zhangliliang/RPN\_BF/tree/RPN-pedestrian][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][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][https_arxiv.org_abs_1610.03466]

Multispectral Deep Neural Networks for Pedestrian Detection

*  intro: BMVC 2016 oral
 *  arxiv: [https://arxiv.org/abs/1611.02644][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][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/][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][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][http_cg.cs.tsinghua.edu.cn_traffic-sign_data_model_code_newdata0411.zip]

## Boundary / Edge / Contour Detection ##

Holistically-Nested Edge Detection

![687474703a2f2f70616765732e756373642e6564752f7e7a74752f6865642e6a7067][]

*  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][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][http_arxiv.org_abs_1504.06375]
 *  github: [https://github.com/s9xie/hed][https_github.com_s9xie_hed]

Unsupervised Learning of Edges

*  intro: CVPR 2016. Facebook AI Research
 *  arxiv: [http://arxiv.org/abs/1511.04166][http_arxiv.org_abs_1511.04166]
 *  zn-blog: [http://www.leiphone.com/news/201607/b1trsg9j6GSMnjOP.html][http_www.leiphone.com_news_201607_b1trsg9j6GSMnjOP.html]

Pushing the Boundaries of Boundary Detection using Deep Learning

*  arxiv: [http://arxiv.org/abs/1511.07386][http_arxiv.org_abs_1511.07386]

Convolutional Oriented Boundaries

*  intro: ECCV 2016
 *  arxiv: [http://arxiv.org/abs/1608.02755][http_arxiv.org_abs_1608.02755]

Richer Convolutional Features for Edge Detection

*  intro: richer convolutional features (RCF)
 *  arxiv: [https://arxiv.org/abs/1612.02103][https_arxiv.org_abs_1612.02103]

## Skeleton Detection ##

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

![687474703a2f2f37786e37777a2e636f6d312e7a302e676c622e636c6f7564646e2e636f6d2f44656570536b656c65746f6e2e706e673f696d61676556696577322f322f772f353030][]

*  arxiv: [http://arxiv.org/abs/1603.09446][http_arxiv.org_abs_1603.09446]
 *  github: [https://github.com/zeakey/DeepSkeleton][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][http_arxiv.org_abs_1609.03659]

## Fruit Detection ##

Deep Fruit Detection in Orchards

*  arxiv: [https://arxiv.org/abs/1610.03677][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/][http_confluence.acfr.usyd.edu.au_display_AGPub]
 *  arxiv: [https://arxiv.org/abs/1610.08120][https_arxiv.org_abs_1610.08120]

## Others ##

Deep Deformation Network for Object Landmark Localization

*  arxiv: [http://arxiv.org/abs/1605.01014][http_arxiv.org_abs_1605.01014]

Fashion Landmark Detection in the Wild

*  arxiv: [http://arxiv.org/abs/1608.03049][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][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][https_cs.stanford.edu_people_ranjaykrishna_vrd_vrd.pdf]
 *  github: [https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection][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”)

![process.png][]

*  github: [https://github.com/geometalab/OSMDeepOD][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][https_arxiv.org_abs_1611.04357]

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

*  arxiv: [https://arxiv.org/abs/1611.05424][https_arxiv.org_abs_1611.05424]

Deep Cuboid Detection: Beyond 2D Bounding Boxes

*  intro: CMU & Magic Leap
 *  arxiv: [https://arxiv.org/abs/1611.10010][https_arxiv.org_abs_1611.10010]

# Object Proposal #

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

*  arxiv: [http://arxiv.org/abs/1510.04445][http_arxiv.org_abs_1510.04445]
 *  github: [https://github.com/aghodrati/deepproposal][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][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][https_arxiv.org_abs_1606.04446]
 *  github: [https://github.com/gidariss/AttractioNet][https_github.com_gidariss_AttractioNet]

Learning to Segment Object Proposals via Recursive Neural Networks

*  arxiv: [https://arxiv.org/abs/1612.01057][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][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][http_www.eecs.berkeley.edu_Pubs_TechRpts_2015_EECS-2015-193.pdf]
 *  github(“SDS using hypercolumns”): [https://github.com/bharath272/sds][https_github.com_bharath272_sds]

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

*  arxiv: [http://arxiv.org/abs/1503.00949][http_arxiv.org_abs_1503.00949]

Weakly Supervised Object Localization Using Size Estimates

*  arxiv: [http://arxiv.org/abs/1608.04314][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][http_www.umiacs.umd.edu_varun_files_refexp-ECCV16.pdf]
 *  github: [https://github.com/varun-nagaraja/referring-expressions][https_github.com_varun-nagaraja_referring-expressions]

LocNet: Improving Localization Accuracy for Object Detection

*  arxiv: [http://arxiv.org/abs/1511.07763][http_arxiv.org_abs_1511.07763]
 *  github: [https://github.com/gidariss/LocNet][https_github.com_gidariss_LocNet]

Learning Deep Features for Discriminative Localization

![framework.jpg][]

*  homepage: [http://cnnlocalization.csail.mit.edu/][http_cnnlocalization.csail.mit.edu]
 *  arxiv: [http://arxiv.org/abs/1512.04150][http_arxiv.org_abs_1512.04150]
 *  github(Tensorflow): [https://github.com/jazzsaxmafia/Weakly\_detector][https_github.com_jazzsaxmafia_Weakly_detector]
 *  github: [https://github.com/metalbubble/CAM][https_github.com_metalbubble_CAM]
 *  github: [https://github.com/tdeboissiere/VGG16CAM-keras][https_github.com_tdeboissiere_VGG16CAM-keras]

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

![model.png][]

*  intro: ECCV 2016
 *  project page: [http://www.di.ens.fr/willow/research/contextlocnet/][http_www.di.ens.fr_willow_research_contextlocnet]
 *  arxiv: [http://arxiv.org/abs/1609.04331][http_arxiv.org_abs_1609.04331]
 *  github: [https://github.com/vadimkantorov/contextlocnet][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][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][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][https_github.com_fmassa_object-detection.torch]

Using DIGITS to train an Object Detection network

![select-object-detection-dataset.jpg][]

*  github: [https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md][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][https_github.com_teaonly_FMD.torch]

# Blogs #

Convolutional Neural Networks for Object Detection

[http://rnd.azoft.com/convolutional-neural-networks-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][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][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][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/][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/][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][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/][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][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][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][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][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][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/][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][https_gist.github.com_bigsnarfdude_2f7b2144065f6056892a98495644d3e0_file-demo_faster_rcnn_notebook-ipynb]
 *  github: [https://github.com/bigsnarfdude/Faster-RCNN\_TF][https_github.com_bigsnarfdude_Faster-RCNN_TF]

[Leaderboard]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#leaderboard
[Papers]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#papers
[R-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#r-cnn
[MultiBox]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#multibox
[SPP-Net]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#spp-net
[DeepID-Net]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#deepid-net
[NoC]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#noc
[Fast R-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#fast-r-cnn
[DeepBox]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#deepbox
[MR-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#mr-cnn
[Faster R-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#faster-r-cnn
[YOLO]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#yolo
[AttentionNet]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#attentionnet
[DenseBox]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#densebox
[SSD]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#ssd
[Inside-Outside Net _ION]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#inside-outside-net-ion
[G-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#g-cnn
[HyperNet]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#hypernet
[MultiPathNet]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#multipathnet
[CRAFT]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#craft
[OHEM]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#ohem
[R-FCN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#r-fcn
[MS-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#ms-cnn
[PVANET]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#pvanet
[GBD-Net]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#gbd-net
[StuffNet]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#stuffnet
[Feature Pyramid Network _FPN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#feature-pyramid-network-fpn
[Detection From Video]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#detection-from-video
[T-CNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#t-cnn
[Datasets]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#datasets
[Object Detection in 3D]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#object-detection-in-3d
[Salient Object Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#salient-object-detection
[Specific Object Deteciton]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#specific-object-deteciton
[Face Deteciton]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#face-deteciton
[UnitBox]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#unitbox
[MTCNN]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#mtcnn
[Datasets _ Benchmarks]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#datasets--benchmarks
[Facial Point _ Landmark Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#facial-point--landmark-detection
[People Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#people-detection
[Person Head Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#person-head-detection
[Pedestrian Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#pedestrian-detection
[Vehicle Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#vehicle-detection
[Traffic-Sign Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#traffic-sign-detection
[Boundary _ Edge _ Contour Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#boundary--edge--contour-detection
[Skeleton Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#skeleton-detection
[Fruit Detection]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#fruit-detection
[Others]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#others
[Object Proposal]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#object-proposal
[Localization]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#localization
[Tutorials]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#tutorials
[Projects]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#projects
[Blogs]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#blogs
[http_host.robots.ox.ac.uk_8080_leaderboard_displaylb.php_challengeid_11_compid_4]: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
[http_papers.nips.cc_paper_5207-deep-neural-networks-for-object-detection.pdf]: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf
[http_arxiv.org_abs_1312.6229]: http://arxiv.org/abs/1312.6229
[https_github.com_sermanet_OverFeat]: https://github.com/sermanet/OverFeat
[http_cilvr.nyu.edu_doku.php_id_software_overfeat_start]: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start
[http_arxiv.org_abs_1311.2524]: http://arxiv.org/abs/1311.2524
[http_people.eecs.berkeley.edu_rbg_papers_r-cnn-cvpr-supp.pdf]: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
[http_www.image-net.org_challenges_LSVRC_2013_slides_r-cnn-ilsvrc2013-workshop.pdf]: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
[http_www.cs.berkeley.edu_rbg_slides_rcnn-cvpr14-slides.pdf]: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
[https_github.com_rbgirshick_rcnn]: https://github.com/rbgirshick/rcnn
[http_zhangliliang.com_2014_07_23_paper-note-rcnn]: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
[https_github.com_BVLC_caffe_pull_482]: https://github.com/BVLC/caffe/pull/482
[http_arxiv.org_abs_1312.2249]: http://arxiv.org/abs/1312.2249
[https_github.com_google_multibox]: https://github.com/google/multibox
[https_research.googleblog.com_2014_12_high-quality-object-detection-at-scale.html]: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html
[http_arxiv.org_abs_1412.1441]: http://arxiv.org/abs/1412.1441
[http_arxiv.org_abs_1406.4729]: http://arxiv.org/abs/1406.4729
[https_github.com_ShaoqingRen_SPP_net]: https://github.com/ShaoqingRen/SPP_net
[http_zhangliliang.com_2014_09_13_paper-note-sppnet]: http://zhangliliang.com/2014/09/13/paper-note-sppnet/
[http_arxiv.org_abs_1407.5736]: http://arxiv.org/abs/1407.5736
[http_www.ee.cuhk.edu.hk_CB_9Cwlouyang_projects_imagenetDeepId_index.html]: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
[http_arxiv.org_abs_1412.5661]: http://arxiv.org/abs/1412.5661
[http_arxiv.org_abs_1412.6856]: http://arxiv.org/abs/1412.6856
[https_www.robots.ox.ac.uk_vgg_rg_papers_zhou_iclr15.pdf]: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
[https_people.csail.mit.edu_khosla_papers_iclr2015_zhou.pdf]: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
[http_places.csail.mit.edu_slide_iclr2015.pdf]: http://places.csail.mit.edu/slide_iclr2015.pdf
[https_www.cs.toronto.edu_yukun_segdeepm.html]: https://www.cs.toronto.edu/~yukun/segdeepm.html
[https_arxiv.org_abs_1502.04275]: https://arxiv.org/abs/1502.04275
[https_github.com_YknZhu_segDeepM]: https://github.com/YknZhu/segDeepM
[http_arxiv.org_abs_1504.06066]: http://arxiv.org/abs/1504.06066
[http_arxiv.org_abs_1504.03293]: http://arxiv.org/abs/1504.03293
[http_www.ytzhang.net_files_publications_2015-cvpr-det-slides.pdf]: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
[https_github.com_YutingZhang_fgs-obj]: https://github.com/YutingZhang/fgs-obj
[http_arxiv.org_abs_1504.08083]: http://arxiv.org/abs/1504.08083
[http_tutorial.caffe.berkeleyvision.org_caffe-cvpr15-detection.pdf]: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
[https_github.com_rbgirshick_fast-rcnn]: https://github.com/rbgirshick/fast-rcnn
[https_github.com_rbgirshick_fast-rcnn_pull_29]: https://github.com/rbgirshick/fast-rcnn/pull/29
[http_zhangliliang.com_2015_05_17_paper-note-fast-rcnn]: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
[http_blog.csdn.net_linj_m_article_details_48930179]: http://blog.csdn.net/linj_m/article/details/48930179
[https_github.com_precedenceguo_mx-rcnn]: https://github.com/precedenceguo/mx-rcnn
[https_github.com_mahyarnajibi_fast-rcnn-torch]: https://github.com/mahyarnajibi/fast-rcnn-torch
[https_github.com_apple2373_chainer-simple-fast-rnn]: https://github.com/apple2373/chainer-simple-fast-rnn
[https_github.com_zplizzi_tensorflow-fast-rcnn]: https://github.com/zplizzi/tensorflow-fast-rcnn
[http_arxiv.org_abs_1505.02146]: http://arxiv.org/abs/1505.02146
[https_github.com_weichengkuo_DeepBox]: https://github.com/weichengkuo/DeepBox
[http_arxiv.org_abs_1505.01749]: http://arxiv.org/abs/1505.01749
[https_github.com_gidariss_mrcnn-object-detection]: https://github.com/gidariss/mrcnn-object-detection
[http_zhangliliang.com_2015_05_17_paper-note-ms-cnn]: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
[http_blog.cvmarcher.com_posts_2015_05_17_multi-region-semantic-segmentation-aware-cnn]: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
[http_arxiv.org_abs_1506.01497]: http://arxiv.org/abs/1506.01497
[http_www.gitxiv.com_posts_8pfpcvefDYn2gSgXk_faster-r-cnn-towards-real-time-object-detection-with-region]: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
[http_web.cs.hacettepe.edu.tr_aykut_classes_spring2016_bil722_slides_w05-FasterR-CNN.pdf]: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
[https_github.com_ShaoqingRen_faster_rcnn]: https://github.com/ShaoqingRen/faster_rcnn
[https_github.com_rbgirshick_py-faster-rcnn]: https://github.com/rbgirshick/py-faster-rcnn
[https_github.com_mitmul_chainer-faster-rcnn]: https://github.com/mitmul/chainer-faster-rcnn
[https_github.com_andreaskoepf_faster-rcnn.torch]: https://github.com/andreaskoepf/faster-rcnn.torch
[https_github.com_ruotianluo_Faster-RCNN-Densecap-torch]: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
[https_github.com_smallcorgi_Faster-RCNN_TF]: https://github.com/smallcorgi/Faster-RCNN_TF
[https_github.com_CharlesShang_TFFRCNN]: https://github.com/CharlesShang/TFFRCNN
[https_github.com_dmlc_mxnet_tree_master_example_rcnn]: https://github.com/dmlc/mxnet/tree/master/example/rcnn
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[http_arxiv.org_abs_1506.02640]: http://arxiv.org/abs/1506.02640
[http_pjreddie.com_darknet_yolo]: http://pjreddie.com/darknet/yolo/
[https_github.com_pjreddie_darknet]: https://github.com/pjreddie/darknet
[https_www.reddit.com_r_MachineLearning_comments_3a3m0o_realtime_object_detection_with_yolo]: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
[https_github.com_gliese581gg_YOLO_tensorflow]: https://github.com/gliese581gg/YOLO_tensorflow
[https_github.com_xingwangsfu_caffe-yolo]: https://github.com/xingwangsfu/caffe-yolo
[https_github.com_frankzhangrui_Darknet-Yolo]: https://github.com/frankzhangrui/Darknet-Yolo
[https_github.com_BriSkyHekun_py-darknet-yolo]: https://github.com/BriSkyHekun/py-darknet-yolo
[https_github.com_tommy-qichang_yolo.torch]: https://github.com/tommy-qichang/yolo.torch
[https_github.com_frischzenger_yolo-windows]: https://github.com/frischzenger/yolo-windows
[https_github.com_AlexeyAB_yolo-windows]: https://github.com/AlexeyAB/yolo-windows
[images-40.jpg]: /images/20220713/b6ceebff2aa14f83ba274640424c2739.png
[http_guanghan.info_blog_en_my-works_train-yolo]: http://guanghan.info/blog/en/my-works/train-yolo/
[https_github.com_Guanghan_darknet]: https://github.com/Guanghan/darknet
[http_arxiv.org_abs_1506.06981]: http://arxiv.org/abs/1506.06981
[http_arxiv.org_abs_1506.07704]: http://arxiv.org/abs/1506.07704
[https_www.robots.ox.ac.uk_vgg_rg_slides_AttentionNet.pdf]: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
[http_image-net.org_challenges_talks_lunit-kaist-slide.pdf]: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf
[http_arxiv.org_abs_1509.04874]: http://arxiv.org/abs/1509.04874
[http_pan.baidu.com_s_1mgoWWsS]: http://pan.baidu.com/s/1mgoWWsS
[http_www.cvlibs.net_datasets_kitti_eval_object.php]: http://www.cvlibs.net/datasets/kitti/eval_object.php
[687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67]: /images/20220713/fb16b95b2bac4733af2d34c7268ece64.png
[http_arxiv.org_abs_1512.02325]: http://arxiv.org/abs/1512.02325
[http_www.cs.unc.edu_wliu_papers_ssd.pdf]: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
[https_github.com_weiliu89_caffe_tree_ssd]: https://github.com/weiliu89/caffe/tree/ssd
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[http_arxiv.org_abs_1608.04314]: http://arxiv.org/abs/1608.04314
[http_www.umiacs.umd.edu_varun_files_refexp-ECCV16.pdf]: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
[https_github.com_varun-nagaraja_referring-expressions]: https://github.com/varun-nagaraja/referring-expressions
[http_arxiv.org_abs_1511.07763]: http://arxiv.org/abs/1511.07763
[https_github.com_gidariss_LocNet]: https://github.com/gidariss/LocNet
[framework.jpg]: /images/20220713/45605b6a7a124426b6d52c1edb1589c6.png
[http_cnnlocalization.csail.mit.edu]: http://cnnlocalization.csail.mit.edu/
[http_arxiv.org_abs_1512.04150]: http://arxiv.org/abs/1512.04150
[https_github.com_jazzsaxmafia_Weakly_detector]: https://github.com/jazzsaxmafia/Weakly_detector
[https_github.com_metalbubble_CAM]: https://github.com/metalbubble/CAM
[https_github.com_tdeboissiere_VGG16CAM-keras]: https://github.com/tdeboissiere/VGG16CAM-keras
[model.png]: /images/20220713/116903063cde4c54a6aeb7d09a1b7eae.png
[http_www.di.ens.fr_willow_research_contextlocnet]: http://www.di.ens.fr/willow/research/contextlocnet/
[http_arxiv.org_abs_1609.04331]: http://arxiv.org/abs/1609.04331
[https_github.com_vadimkantorov_contextlocnet]: https://github.com/vadimkantorov/contextlocnet
[http_research.microsoft.com_en-us_um_people_kahe_iccv15tutorial_iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf]: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf
[ReInspect]: https://github.com/Russell91/ReInspect/
[https_github.com_Russell91_TensorBox]: https://github.com/Russell91/TensorBox
[https_github.com_fmassa_object-detection.torch]: https://github.com/fmassa/object-detection.torch
[select-object-detection-dataset.jpg]: /images/20220713/05cf060dafc84ded8d02d73030a0a634.png
[https_github.com_NVIDIA_DIGITS_blob_master_examples_object-detection_README.md]: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md
[https_github.com_teaonly_FMD.torch]: https://github.com/teaonly/FMD.torch
[http_rnd.azoft.com_convolutional-neural-networks-object-detection]: http://rnd.azoft.com/convolutional-neural-networks-object-detection/
[https_engineering.pinterest.com_blog_introducing-automatic-object-detection-visual-search]: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
[https_engineering.pinterest.com_sites_engineering_files_Visual_20Search_20V1_20-_20Video.mp4]: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
[https_news.developer.nvidia.com_pinterest-introduces-the-future-of-visual-search_mkt_tok_eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0_3D]: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D
[https_devblogs.nvidia.com_parallelforall_deep-learning-object-detection-digits]: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/
[https_adeshpande3.github.io_adeshpande3.github.io_Analyzing-the-Papers-Behind-Facebook_s-Computer-Vision-Approach]: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook'%20rel=
[http_blog.dlib.net_2016_10_easily-create-high-quality-object.html]: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html
[https_blogs.technet.microsoft.com_machinelearning_2016_10_25_how-to-train-a-deep-learned-object-detection-model-in-cntk]: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
[https_github.com_Microsoft_CNTK_tree_master_Examples_Image_Detection_FastRCNN]: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN
[https_medium.com_the-downlinq_object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7_.2csh4iwx9]: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
[https_medium.com_the-downlinq_object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92_.f9b7dgf64]: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64
[https_medium.com_the-downlinq_you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571_.fmmi2o3of]: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
[https_medium.com_the-downlinq_you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588_.nwzarsz1t]: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t
[https_bigsnarf.wordpress.com_2016_11_07_faster-r-cnn-pedestrian-and-car-detection]: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
[https_gist.github.com_bigsnarfdude_2f7b2144065f6056892a98495644d3e0_file-demo_faster_rcnn_notebook-ipynb]: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
[https_github.com_bigsnarfdude_Faster-RCNN_TF]: https://github.com/bigsnarfdude/Faster-RCNN_TF