https://github.com/smorodov/deep-learning-object-detection-links.
Object detectors based on DL (from: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html )
https://github.com/smorodov/deep-learning-object-detection-links.
Last synced: 3 months ago
JSON representation
Object detectors based on DL (from: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html )
- Host: GitHub
- URL: https://github.com/smorodov/deep-learning-object-detection-links.
- Owner: Smorodov
- Created: 2017-02-18T16:31:54.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-02-18T17:04:53.000Z (about 8 years ago)
- Last Synced: 2024-12-26T15:11:53.445Z (5 months ago)
- Homepage:
- Size: 269 KB
- Stars: 233
- Watchers: 23
- Forks: 99
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
## Object Detection
|Method|VOC2007|VOC2010|VOC2012|ILSVRC 2013|MSCOCO 2015|Speed|
|--- |--- |--- |--- |--- |--- |--- |
|OverFeat|-|-|-|24.3%|-|-|
|R-CNN (AlexNet)|58.5%|53.7%|53.3%|31.4%|-|-|
|R-CNN (VGG16)|66.0%|-|-|-|-|-|
|SPP_net(ZF-5)|54.2%(1-model), 60.9%(2-model)|-|-|31.84%(1-model), 35.11%(6-model)|-|-|
|DeepID-Net|64.1%|-|-|50.3%|-|-|
|NoC|73.3%|-|68.8%|-|-|-|
|Fast-RCNN (VGG16)|70.0%|68.8%|68.4%|-|19.7%(@[0.5-0.95]), 35.9%(@0.5)|-|
|MR-CNN|78.2%|-|73.9%|-|-|-|
|Faster-RCNN (VGG16)|78.8%|-|75.9%|-|21.9%(@[0.5-0.95]), 42.7%(@0.5)|198ms|
|Faster-RCNN (ResNet-101)|85.6%|-|83.8%|-|37.4%(@[0.5-0.95]), 59.0%(@0.5)|-|
|SSD300 (VGG16)|72.1%|-|-|-|-|58 fps|
|SSD500 (VGG16)|75.1%|-|-|-|-|23 fps|
|ION|79.2%|-|76.4%|-|-|-|
|CRAFT|75.7%|-|71.3%|48.5%|-|-|
|OHEM|78.9%|-|76.3%|-|25.5%(@[0.5-0.95]), 45.9%(@0.5)|-|
|R-FCN (ResNet-50)|77.4%|-|-|-|-|0.12sec(K40), 0.09sec(TitianX)|
|R-FCN (ResNet-101)|79.5%|-|-|-|-|0.17sec(K40), 0.12sec(TitianX)|
|R-FCN (ResNet-101),multi sc train|83.6%|-|82.0%|-|31.5%(@[0.5-0.95]), 53.2%(@0.5)|-|
|PVANet 9.0|81.8%|-|82.5%|-|-|750ms(CPU), 46ms(TitianX)|
# Leaderboard
**Detection Results: VOC2012**
* intro: Competition “comp4” (train on additional 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**
* 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: first 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: second 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/)
## 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)
**Contextual Priming and Feedback for Faster R-CNN**
* intro: ECCV 2016\. Carnegie Mellon University
* paper: [http://abhinavsh.info/context_priming_feedback.pdf](http://abhinavsh.info/context_priming_feedback.pdf)
* poster: [http://www.eccv2016.org/files/posters/P-1A-20.pdf](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](https://arxiv.org/abs/1702.02138)
* github: [https://github.com/endernewton/tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn)
## YOLO
**You Only Look Once: Unified, Real-Time Object Detection**
* 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)
**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](https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp)
* github: [https://github.com/thtrieu/darkflow](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/](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**
* intro: ECCV 2016 Oral
* 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)
* slides: [http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf](http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.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)
## 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**
* 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)
* github（Official）: [https://github.com/abhi2610/ohem](https://github.com/abhi2610/ohem)
* author page: [http://abhinav-shrivastava.info/](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](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](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: WACV 2017\. 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](https://arxiv.org/abs/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)
* slides: [http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning](http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning)
* github: [https://github.com/imatge-upc/detection-2016-nipsws](https://github.com/imatge-upc/detection-2016-nipsws)
* blog: [http://jorditorres.org/nips/](http://jorditorres.org/nips/)
**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)
* github: [https://github.com/BichenWuUCB/squeezeDet](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](https://arxiv.org/abs/1612.03144)
**Action-Driven Object Detection with Top-Down Visual Attentions**
* arxiv: [https://arxiv.org/abs/1612.06704](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](https://arxiv.org/abs/1612.06851)
## YOLOv2
**YOLO9000: Better, Faster, Stronger**
* arxiv: [https://arxiv.org/abs/1612.08242](https://arxiv.org/abs/1612.08242)
* code: [http://pjreddie.com/yolo9000/](http://pjreddie.com/yolo9000/)
* github(Chainer): [https://github.com/leetenki/YOLOv2](https://github.com/leetenki/YOLOv2)
## DSSD
**DSSD : Deconvolutional Single Shot Detector**
* intro: UNC Chapel Hill & Amazon Inc
* arxiv: [https://arxiv.org/abs/1701.06659](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](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](https://arxiv.org/abs/1702.01478)
# 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)
# Object Detection on RGB-D
**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)
**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](http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html)
# 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**
* 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**
* 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**
* 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)
**SalGAN: Visual Saliency Prediction with Generative Adversarial Networks**
* project page: [https://imatge-upc.github.io/saliency-salgan-2017/](https://imatge-upc.github.io/saliency-salgan-2017/)
* arxiv: [https://arxiv.org/abs/1701.01081](https://arxiv.org/abs/1701.01081)
**Visual Saliency Prediction Using a Mixture of Deep Neural Networks**
* arxiv: [https://arxiv.org/abs/1702.00372](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](https://arxiv.org/abs/1702.00615)
## Saliency Detection in Video
**Deep Learning For Video Saliency Detection**
* arxiv: [https://arxiv.org/abs/1702.00871](https://arxiv.org/abs/1702.00871)
## Datasets
**MSRA10K Salient Object Database**
[http://mmcheng.net/msra10k/](http://mmcheng.net/msra10k/)
# 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**
* 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)
**CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection**
* intro: CMU
* arxiv: [https://arxiv.org/abs/1606.05413](https://arxiv.org/abs/1606.05413)
**Finding Tiny Faces**
* intro: CMU
* arxiv: [https://arxiv.org/abs/1612.04402](https://arxiv.org/abs/1612.04402)
**Towards a Deep Learning Framework for Unconstrained Face Detection**
* intro: overlap with CMS-RCNN
* arxiv: [https://arxiv.org/abs/1612.05322](https://arxiv.org/abs/1612.05322)
**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**
* 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)
* github(MXNet): [https://github.com/Seanlinx/mtcnn](https://github.com/Seanlinx/mtcnn)
**Face Detection using Deep Learning: An Improved Faster RCNN Approach**
* intro: DeepIR Inc
* arxiv: [https://arxiv.org/abs/1701.08289](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](https://arxiv.org/abs/1701.08393)
### 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**
* 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**
* 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)
**Facial Landmark Detection by Deep Multi-task Learning**
* intro: ECCV 2014
* project page: [http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html](http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html)
* paper: [http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf](http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf)
* github(Matlab): [https://github.com/zhzhanp/TCDCN-face-alignment](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](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)
**Effective face landmark localization via single deep network**
* arxiv: [https://arxiv.org/abs/1702.02719](https://arxiv.org/abs/1702.02719)
## People Detection
**End-to-end people detection in crowded scenes**
* 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)
**Scale-aware Fast R-CNN for Pedestrian Detection**
* arxiv: [https://arxiv.org/abs/1510.08160](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](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?**
* intro: ECCV 2016
* 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)
**Evolving Boxes for fast Vehicle Detection**
* arxiv: [https://arxiv.org/abs/1702.00254](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/](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**
* 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)
**Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks**
* project page: [http://www.vision.ee.ethz.ch/~cvlsegmentation/](http://www.vision.ee.ethz.ch/~cvlsegmentation/)
* arxiv: [https://arxiv.org/abs/1701.04658](https://arxiv.org/abs/1701.04658)
**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**
* 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”)**
* 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)
**Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection**
* arxiv: [https://arxiv.org/abs/1612.03019](https://arxiv.org/abs/1612.03019)
**Deep Learning Logo Detection with Data Expansion by Synthesising Context**
* arxiv: [https://arxiv.org/abs/1612.09322](https://arxiv.org/abs/1612.09322)
**Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks**
* arxiv: [https://arxiv.org/abs/1702.00307](https://arxiv.org/abs/1702.00307)
# 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: BMVC 2016\. 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)
**Active Object Localization with Deep Reinforcement Learning**
* intro: ICCV 2015
* keywords: Markov Decision Process
* arxiv: [https://arxiv.org/abs/1511.06015](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](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**
* 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**
* 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 / 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](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](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](https://github.com/Russell91/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**
* 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)
**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](https://github.com/MarvinTeichmann/KittiBox)
# 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)
**Small U-Net for vehicle detection**
* blog: [https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad](https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad)