机器学习和深度学习资源汇总（陆续更新）
不多说，直接上干货！
本篇博客的目地，是对工作学习过程中所遇所见的一些有关深度学习、机器学习的优质资源，作分类汇总，方便自己查阅，也方便他人学习借用。
主要会涉及一些优质的理论书籍和论文、一些实惠好用的工具库和开源库、一些供入门该理论入门所用的demo等等。
由于本博客将不定期更新，尽量将较为前沿的深度学习、机器学习内容整理下来，需要转载的同学尽量附上本文的链接，方便获得最新的内容。
机器学习领域相关的大牛推荐（陆续更新）
- 相关的理论、书籍、论文、课程、博客：
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- [Book] Yoshua Bengio, Ian Goodfellow, Aaron Courville. Deep Learning. 2015.
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- [Book] Michael ielsen. eural etworks and Deep Learning. 2015.
- [Course] Convolutional eural etworks for Visual Recognition. 2015
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- [Course] Deep Learning for atural Language Processing. 2015.
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- [Blog] Hacker’s Guide to eural etworks.
- [Book] otes on Convolutional eural etworks
- [Book] A guide to convolution arithmetic for deep learning
- 相关的库、工具
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- Theano (Python)
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- Libraries based on Theano: Lasagne, Keras, Pylearn2
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- Caffe (C, with Python wrapper)
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- TensorFlow (Python, C)
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- Torch (Lua)
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- ConvetJS (Javascript)
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- Deeplearning4j (Java)
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- MatConvet (Matlab)
- Spark machine learning library(Java,scala,python)
- LIBSVM A Library for Support Vector Machines(C/C,Java and other languages)
- 相关的开源项目、demo
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- Facial Keypoint Detection
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- Deep Dream
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- Eyescream
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- Deep Q-network (Atari game player)
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- Caffe to Theano Model Conversion (use Caffe pretrained model in Lasagne)
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- R-C
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- Fast R-C
| Method | VOC2007 | VOC2010 | VOC2012 | ILSVRC 201 | MSCOCO 2015 | Speed |
|---|---|---|---|---|---|---|
| OverFeat | 24.% | |||||
| R-C (Alexet) | 58.5% | 5.7% | 5.% | 1.4% | ||
| R-C (VGG16) | 66.0% | |||||
| SPP_net(ZF-5) | 54.2%(1-model), 60.9%(2-model) | 1.84%(1-model), 5.11%(6-model) | ||||
| DeepID-et | 64.1% | 50.% | ||||
| oC | 7.% | 68.8% | ||||
| Fast-RC (VGG16) | 70.0% | 68.8% | 68.4% | 19.7%(@[0.5-0.95]), 5.9%(@0.5) | ||
| MR-C | 78.2% | 7.9% | ||||
| Faster-RC (VGG16) | 78.8% | 75.9% | 21.9%(@[0.5-0.95]), 42.7%(@0.5) | 198ms | ||
| Faster-RC (Reset-101) | 85.6% | 8.8% | 7.4%(@[0.5-0.95]), 59.0%(@0.5) | |||
| SSD00 (VGG16) | 77.2% | 75.8% | 25.1%(@[0.5-0.95]), 4.1%(@0.5) | 46 fps | ||
| SSD512 (VGG16) | 79.8% | 78.5% | 28.8%(@[0.5-0.95]), 48.5%(@0.5) | 19 fps | ||
| IO | 79.2% | 76.4% | ||||
| CRAFT | 75.7% | 71.% | 48.5% | |||
| OHEM | 78.9% | 76.% | 25.5%(@[0.5-0.95]), 45.9%(@0.5) | |||
| R-FC (Reset-50) | 77.4% | 0.12sec(K40), 0.09sec(TitianX) | ||||
| R-FC (Reset-101) | 79.5% | 0.17sec(K40), 0.12sec(TitianX) | ||||
| R-FC (Reset-101),multi sc train | 8.6% | 82.0% | 1.5%(@[0.5-0.95]), 5.2%(@0.5) | |||
| PVAet 9.0 | 89.8% | 84.2% | 750ms(CPU), 46ms(TitianX) |
Leaderboard
Detection Results: VOC2012
- intro: Competition “comp4” (train on additional data)
- homepage: http://host.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
Papers
Deep eural etworks for Object Detection
- paper: http:///paper/5207-deep-neural-networks-for-object-detection.pdf
OverFeat: Integrated Recognition, Localization and Detection using Convolutional etworks
- arxiv: http://arxiv/abs/112.6229
- github: https://github/sermanet/OverFeat
- code: http:///doku.php?id=software:overfeat:start
R-C
Rich feature hierarchies for accurate object detection and semantic segmentation
- intro: R-C
- arxiv: http://arxiv/abs/111.2524
- supp: http:///~rbg/papers/r-cnn-cvpr-supp.pdf
- slides: http://www.image-net/challenges/LSVRC/201/slides/r-cnn-ilsvrc201-workshop.pdf
- slides: http:///~rbg/slides/rcnn-cvpr14-slides.pdf
- github: https://github/rbgirshick/rcnn
- notes: http://zhangliliang/2014/07/2/paper-note-rcnn/
- caffe-pr(“Make R-C the Caffe detection example”): https://github/BVLC/caffe/pull/482
MultiBox
Scalable Object Detection using Deep eural etworks
- intro: first MultiBox. Train a C to predict Region of Interest.
- arxiv: http://arxiv/abs/112.2249
- github: https://github/google/multibox
- blog: https://research.googleblog/2014/12/high-quality-object-detection-at-scale.html
Scalable, High-Quality Object Detection
- intro: second MultiBox
- arxiv: http://arxiv/abs/1412.1441
- github: https://github/google/multibox
SPP-et
Spatial Pyramid Pooling in Deep Convolutional etworks for Visual Recognition
- intro: ECCV 2014 / TPAMI 2015
- arxiv: http://arxiv/abs/1406.4729
- github: https://github/ShaoqingRen/SPP_net
- notes: http://zhangliliang/2014/09/1/paper-note-sppnet/
DeepID-et
DeepID-et: Deformable Deep Convolutional eural etworks for Object Detection
- intro: PAMI 2016
- intro: an extension of R-C. box pre-training, cascade on region proposals, deformation layers and context representati
- project page: http://hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
- arxiv: http://arxiv/abs/1412.5661
Object Detectors Emerge in Deep Scene Cs
- arxiv: http://arxiv/abs/1412.6856
- paper: https://www.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
- paper: https:///khosla/papers/iclr2015_zhou.pdf
- slides: http:///slide_iclr2015.pdf
segDeepM: Exploiting Segmentation and Context in Deep eural etworks for Object Detection
- intro: CVPR 2015
- project(codedata): https://toronto.edu/~yukun/segdeepm.html
- arxiv: https://arxiv/abs/1502.04275
- github: https://github/YknZhu/segDeepM
oC
Object Detection etworks on Convolutional Feature Maps
- intro: TPAMI 2015
- arxiv: http://arxiv/abs/1504.06066
Improving Object Detection with Deep Convolutional etworks via Bayesian Optimization and Structured Prediction
- arxiv: http://arxiv/abs/1504.029
- slides: http://www.ytzhang/files/publicati/2015-cvpr-det-slides.pdf
- github: https://github/YutingZhang/fgs-obj
Fast R-C
Fast R-C
- arxiv: http://arxiv/abs/1504.0808
- slides: http://berkeleyvision/caffe-cvpr15-detection.pdf
- github: https://github/rbgirshick/fast-rcnn
- github(COCO-branch): https://github/rbgirshick/fast-rcnn/tree/coco
- webcam demo: https://github/rbgirshick/fast-rcnn/pull/29
- notes: http://zhangliliang/2015/05/17/paper-note-fast-rcnn/
- notes: http:///linj_m/article/details/4890179
- github(“Fast R-C in MXet”): https://github/precedenceguo/mx-rcnn
- github: https://github/mahyarnajibi/fast-rcnn-torch
- github: https://github/apple27/chainer-simple-fast-rnn
- github(Tensorflow): https://github/zplizzi/tensorflow-fast-rcnn
DeepBox
DeepBox: Learning Objectness with Convolutional etworks
- arxiv: http://arxiv/abs/1505.02146
- github: https://github/weichengkuo/DeepBox
MR-C
Object detection via a multi-region & semantic segmentation-aware C model
- intro: ICCV 2015. MR-C
- arxiv: http://arxiv/abs/1505.01749
- github: https://github/gidariss/mrcnn-object-detection
- notes: http://zhangliliang/2015/05/17/paper-note-ms-cnn/
- notes: http:///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-C
Faster R-C: Towards Real-Time Object Detection with Region Proposal etworks
- intro: IPS 2015
- arxiv: http://arxiv/abs/1506.01497
- gitxiv: http://www.gitxiv/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
- slides: http:///~aykut/classes/spring2016/bil722/slides/w05-FasterR-C.pdf
- github(official, Matlab): https://github/ShaoqingRen/faster_rcnn
- github: https://github/rbgirshick/py-faster-rcnn
- github: https://github/mitmul/chainer-faster-rcnn
- github: https://github/andreaskoepf/
- github: https://github/ruotianluo/Faster-RC-Densecap-torch
- github: https://github/smallcorgi/Faster-RC_TF
- github: https://github/CharlesShang/TFFRC
- github(C demo): https://github/YihangLou/FasterRC-Encapsulation-Cplusplus
- github: https://github/yhenon/keras-frcnn
Faster R-C in MXet with distributed implementation and data parallelization
- github: https://github/dmlc/mxnet/tree/master/example/rcnn
Contextual Priming and Feedback for Faster R-C
- intro: ECCV 2016. Carnegie Mellon University
- paper: http://abhinavsh.info/context_priming_feedback.pdf
- poster: http:///files/posters/P-1A-20.pdf
An Implementation of Faster RC with Study for Region Sampling
- intro: Technical Report, pages. CMU
- arxiv: https://arxiv/abs/1702.0218
- github: https://github/endernewton/tf-faster-rcnn
YOLO
You Only Look Once: Unified, Real-Time Object Detection
- arxiv: http://arxiv/abs/1506.02640
- code: http://pjreddie/darknet/yolo/
- github: https://github/pjreddie/darknet
- blog: https://pjreddie/publicati/yolo/
- slides: https://docs.google/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=000&slide=id.p
- reddit: https://www.reddit/r/MachineLearning/comments/am0o/realtime_object_detection_with_yolo/
- github: https://github/gliese581gg/YOLO_tensorflow
- github: https://github/xingwangsfu/caffe-yolo
- github: https://github/frankzhangrui/Darknet-Yolo
- github: https://github/BriSkyHekun/py-darknet-yolo
- github: https://github/tommy-qichang/
- github: https://github/frischzenger/yolo-windows
- gtihub: https://github/AlexeyAB/yolo-windows
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export ctant graph def to C
- blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
- github: https://github/thtrieu/darkflow
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: http://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github/Guanghan/darknet
R-C minus R
- arxiv: http://arxiv/abs/1506.06981
Attentionet
Attentionet: Aggregating Weak Directi 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/abs/1506.07704
- slides: https://www.ac.uk/~vgg/rg/slides/Attentionet.pdf
- slides: http://image-net/challenges/talks/lunit-kaist-slide.pdf
DenseBox
DenseBox: Unifying Landmark Localization with End to End Object Detection
- arxiv: http://arxiv/abs/1509.04874
- demo: http://pan.baidu/s/1mgoWWsS
- KITTI result: http:///datasets/kitti/eval_object.php
SSD
SSD: Single Shot MultiBox Detector
- intro: ECCV 2016 Oral
- arxiv: http://arxiv/abs/1512.0225
- paper: http:///~wliu/papers/ssd.pdf
- slides: http:///%7Ewliu/papers/ssd_eccv2016_slide.pdf
- github: https://github/weiliu89/caffe/tree/ssd
- video: http://weibo/p/204447a226da96254c96c97fb05dda97
- github: https://github/zhreshold/mxnet-ssd
- github: https://github/zhreshold/
- github: https://github/rykov8/ssd_keras
- github: https://github/balancap/SSD-Tensorflow
- github: https://github/amdegroot/ssd.pytorch
Inside-Outside et (IO)
Inside-Outside et: Detecting Objects in Context with Skip Pooling and Recurrent eural etworks
- 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/abs/1512.0414
- slides: http://www./tmp/ion-coco-talk-bell2015.pdf
- coco-leaderboard: http://mscoco/dataset/#detecti-leaderboard
Adaptive Object Detection Using Adjacency and Zoom Prediction
- intro: CVPR 2016. AZ-et
- arxiv: http://arxiv/abs/1512.07711
- github: https://github/luyongxi/az-net
- youtube: https://www.youtube/watch?v=YmFtuwxaM
G-C
G-C: an Iterative Grid Based Object Detector
- arxiv: http://arxiv/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 rd for provided data and 2nd for external data on ILSVRC 2015 object detection
- project page: http://hk/~wlouyang/projects/ImageetFactors/CVPR16.html
- arxiv: http://arxiv/abs/1601.05150
We don’t need no bounding-boxes: Training object class detectors using only human verification
- arxiv: http://arxiv/abs/1602.08405
Hyperet
Hyperet: Towards Accurate Region Proposal Generation and Joint Object Detection
- arxiv: http://arxiv/abs/1604.00600
MultiPathet
A MultiPath etwork for Object Detection
- intro: BMVC 2016. Facebook AI Research (FAIR)
- arxiv: http://arxiv/abs/1604.0215
- github: https://github/facebookresearch/multipathnet
CRAFT
CRAFT Objects from Images
- intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-C
- project page: http://byangderek.github.io/projects/craft.html
- arxiv: https://arxiv/abs/1604.029
- paper: http:///openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
- github: https://github/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/abs/1604.0540
- paper: http:///openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
- github（Official）: https://github/abhi2610/ohem
- author page: http://abhinav-shrivastava.info/
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
- intro: CVPR 2016
- arxiv: http://arxiv/abs/1604.05766
Exploit All the Layers: Fast and Accurate C Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
- intro: scale-dependent pooling (SDP), cascaded rejection clas-sifiers (CRC)
- paper: http://www-personal./~wgchoi/SDP-CRC_camready.pdf
R-FC
R-FC: Object Detection via Region-based Fully Convolutional etworks
- arxiv: http://arxiv/abs/1605.06409
- github: https://github/daijifeng001/R-FC
- github: https://github/Orpine/py-R-FC
- github(PyTorch): https://github/PureDiors/pytorch_RFC
- github: https://github/bharatsingh40/py-R-FC-multiGPU
Weakly supervised object detection using pseudo-strong labels
- arxiv: http://arxiv/abs/1607.0471
Recycle deep features for better object detection
- arxiv: http://arxiv/abs/1607.05066
MS-C
A Unified Multi-scale Deep Convolutional eural etwork for Fast Object Detection
- intro: ECCV 2016
- intro: 640×480: 15 fps, 960×720: 8 fps
- arxiv: http://arxiv/abs/1607.07155
- github: https://github/zhaoweicai/mscnn
- poster: http:///files/posters/P-2B-8.pdf
Multi-stage Object Detection with Group Recursive Learning
- intro: VOC2007: 78.6%, VOC2012: 74.9%
- arxiv: http://arxiv/abs/1608.05159
Subcategory-aware Convolutional eural etworks for Object Proposals and Detection
- intro: WACV 2017. SubC
- arxiv: http://arxiv/abs/1604.0469
- github: https://github/yuxng/SubC
PVAET
PVAET: Deep but Lightweight eural etworks for Real-time Object Detection
- intro: “less channels with more layers”, concatenated ReLU, Inception, and Hyperet, batch normalization, residual connecti
- arxiv: http://arxiv/abs/1608.08021
- github: https://github/sanghoon/pva-faster-rcnn
- leaderboard(PVAet 9.0): http://host.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
PVAet: Lightweight Deep eural etworks for Real-time Object Detection
- intro: Presented at IPS 2016 Workshop on Efficient Methods for Deep eural etworks (EMD). Continuation ofarXiv:1608.08021
- arxiv: https://arxiv/abs/1611.08588
GBD-et
Gated Bi-directional C for Object Detection
- intro: The Chinese University of Hong Kong & Sensetime Group Limited
- paper: http://link.springer/chapter/10.1007/978--19-46478-7_22
- mirror: https://pan.baidu/s/1dFohO7v
Crafting GBD-et for Object Detection
- intro: winner of the Imageet object detection challenge of 2016. CUImage and CUVideo
- intro: gated bi-directional C (GBD-et)
- arxiv: https://arxiv/abs/1610.02579
- github: https://github/craftGBD/craftGBD
Stuffet
Stuffet: Using ‘Stuff’ to Improve Object Detection
- arxiv: https://arxiv/abs/1610.05861
Generalized Haar Filter based Deep etworks for Real-Time Object Detection in Traffic Scene
- arxiv: https://arxiv/abs/1610.09609
Hierarchical Object Detection with Deep Reinforcement Learning
- intro: Deep Reinforcement Learning Workshop (IPS 2016)
- project page: https://imatge-upc.github.io/detection-2016-nipsws/
- arxiv: https://arxiv/abs/1611.0718
- slides: http://www.slideshare/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
- github: https://github/imatge-upc/detection-2016-nipsws
- blog: http://jorditorres/nips/
Learning to detect and localize many objects from few examples
- arxiv: https://arxiv/abs/1611.05664
Speed/accuracy trade-offs for modern convolutional object detectors
- intro: Google Research
- arxiv: https://arxiv/abs/1611.10012
SqueezeDet: Unified, Small, Low Power Fully Convolutional eural etworks for Real-Time Object Detection for Autonomous Driving
- arxiv: https://arxiv/abs/1612.01051
- github: https://github/BichenWuUCB/squeezeDet
Feature Pyramid etwork (FP)
Feature Pyramid etworks for Object Detection
- intro: Facebook AI Research
- arxiv: https://arxiv/abs/1612.0144
Action-Driven Object Detection with Top-Down Visual Attenti
- arxiv: https://arxiv/abs/1612.06704
Beyond Skip Connecti: Top-Down Modulation for Object Detection
- intro: CMU & UC Berkeley & Google Research
- arxiv: https://arxiv/abs/1612.06851
YOLOv2
YOLO9000: Better, Faster, Stronger
- arxiv: https://arxiv/abs/1612.08242
- code: http://pjreddie/yolo9000/
- github(Chainer): https://github/leetenki/YOLOv2
- github(Keras): https://github/allanzelener/YAD2K
- github(PyTorch): https://github/longcw/yolo2-pytorch
- github(Tensorflow): https://github/hizhangp/yolo_tensorflow
- github(Windows): https://github/AlexeyAB/darknet
- github: https://github/choasUp/caffe-yolo9000
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
- github: https://github/AlexeyAB/Yolo_mark
DSSD
DSSD : Deconvolutional Single Shot Detector
- intro: UC Chapel Hill & Amazon Inc
- arxiv: https://arxiv/abs/1701.06659
Wide-Residual-Inception etworks for Real-time Object Detection
- intro: Inha University
- arxiv: https://arxiv/abs/1702.0124
Attentional etwork for Visual Object Detection
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: https://arxiv/abs/1702.01478
CC-et
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
- intro: chained cascade network (CC-et). 81.1% mAP on PASCAL VOC 2007
- arxiv: https://arxiv/abs/1702.07054
Deet: Scalable Real-time Object Detection with Directed Sparse Sampling
https://arxiv/abs/170.10295
A-Fast-RC: Hard Positive Generation via Adversary for Object Detection
- intro: CVPR 2017
- paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
- github(Caffe): https://github/xiaolonw/adversarial-frcnn
Discriminative Bimodal etworks for Visual Localization and Detection with atural Language Queries
- intro: CVPR 2017
- arxiv: https://arxiv/abs/1704.0944
Spatial Memory for Context Reasoning in Object Detection
- arxiv: https://arxiv/abs/1704.04224
Improving Object Detection With One Line of Code
- intro: University of Maryland
- keywords: Soft-MS
- arxiv: https://arxiv/abs/1704.0450
- github: https://github/bharatsingh40/soft-nms
Accurate Single Stage Detector Using Recurrent Rolling Convolution
- intro: CVPR 2017
- arxiv: https://arxiv/abs/1704.05776
- github: https://github/xiaohaoChen/rrc_detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
https://arxiv/abs/1704.05775
Detection From Video
Learning Object Class Detectors from Weakly Annotated Video
- intro: CVPR 2012
- paper: https://www./publicati/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
- arxiv: https://arxiv/abs/1501.01186
Video Object Recognition
- slides: http://vision./courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx
Deep Learning for Saliency Prediction in atural Video
- intro: Submitted on 12 Jan 2016
- keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
- paper: https://hal.archives-ouvertes.fr/hal-01251614/document
T-C
T-C: Tubelets with Convolutional eural etworks for Object Detection from Videos
- intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
- arxiv: http://arxiv/abs/1604.0252
- github: https://github/myfavouritekk/T-C
Object Detection from Video Tubelets with Convolutional eural etworks
- intro: CVPR 2016 Spotlight paper
- arxiv: https://arxiv/abs/1604.0405
- paper: http://hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
- gihtub: https://github/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
- intro: SenseTime Group
- slides: http://hk/~xgwang/CUvideo.pdf
- slides: http://image-net/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf
Context Matters: Refining Object Detection in Video with Recurrent eural etworks
- intro: BMVC 2016
- keywords: pseudo-labeler
- arxiv: http://arxiv/abs/1607.04648
- paper: http:///se/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
C Based Object Detection in Large Video Images
- intro: WangTao @ 爱奇艺
- keywords: object retrieval, object detection, scene classification
- slides: http://on-demand.gputechconf/gtc/2016/presentation/s662-wang-tao-cnn-based-object-detection-large-video-images.pdf
Object Detection in Videos with Tubelet Proposal etworks
- arxiv: https://arxiv/abs/1702.0655
Flow-Guided Feature Aggregation for Video Object Detection
- intro: MSRA
- arxiv: https://arxiv/abs/170.10025
Video Object Detection using Faster R-C
- blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
- github: https://github/andrewliao11/py-faster-rcnn-imagenet
Object Detection in D
VoteDeep: Fast Object Detection in D Point Clouds Using Efficient Convolutional eural etworks
- arxiv: https://arxiv/abs/1609.06666
Object Detection on RGB-D
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
- arxiv: http://arxiv/abs/1407.576
Differential Geometry Boosts Convolutional eural etworks for Object Detection
- intro: CVPR 2016
- paper: http:///openaccess/content_cvpr_2016_workshops/w2/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
https://arxiv/abs/170.047
Salient Object Detection
This task involves predicting the salient regi of an image given by human eye fixati.
Best Deep Saliency Detection Models (CVPR 2016 & 2015)
http://hku.hk/~yzyu/vision.html
Large-scale optimization of hierarchical features for saliency prediction in natural images
- paper: http://coxlab/pdfs/cvpr2014_vig_saliency.pdf
Predicting Eye Fixati using Convolutional eural etworks
- paper: http:///system/file?fileId=72648
Saliency Detection by Multi-Context Deep Learning
- paper: http:///openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf
DeepSaliency: Multi-Task Deep eural etwork Model for Salient Object Detection
- arxiv: http://arxiv/abs/1510.05484
SuperC: A Superpixelwise Convolutional eural etwork for Salient Object Detection
- paper: www.shengfenghe/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html
Shallow and Deep Convolutional etworks for Saliency Prediction
- arxiv: http://arxiv/abs/160.00845
- github: https://github/imatge-upc/saliency-2016-cvpr
Recurrent Attentional etworks for Saliency Detection
- intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACD)
- arxiv: http://arxiv/abs/1604.0227
Two-Stream Convolutional etworks for Dynamic Saliency Prediction
- arxiv: http://arxiv/abs/1607.0470
Unctrained Salient Object Detection
Unctrained Salient Object Detection via Proposal Subset Optimization
- intro: CVPR 2016
- project page: http://cs-people./jmzhang/sod.html
- paper: http://cs-people./jmzhang/SOD/CVPR16SOD_camera_ready.pdf
- github: https://github/jimmie/SOD
- caffe model zoo: https://github/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection
DHSet: Deep Hierarchical Saliency etwork for Salient Object Detection
- paper: http:///openaccess/content_cvpr_2016/papers/Liu_DHSet_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./jmzhang/sos.html
- arxiv: http://arxiv/abs/1607.07525
- paper: http://cs-people./jmzhang/SOS/SOS_preprint.pdf
- caffe model zoo: https://github/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing
Deeply-Supervised Recurrent Convolutional eural etwork for Saliency Detection
- intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRC)
- arxiv: http://arxiv/abs/1608.05177
Saliency Detection via Combining Region-Level and Pixel-Level Predicti with Cs
- intro: ECCV 2016
- arxiv: http://arxiv/abs/1608.05186
Edge Preserving and Multi-Scale Contextual eural etwork for Salient Object Detection
- arxiv: http://arxiv/abs/1608.08029
A Deep Multi-Level etwork for Saliency Prediction
- arxiv: http://arxiv/abs/1609.01064
Visual Saliency Detection Based on Multiscale Deep C Features
- intro: IEEE Transacti on Image Processing
- arxiv: http://arxiv/abs/1609.02077
A Deep Spatial Contextual Long-term Recurrent Convolutional etwork for Saliency Detection
- intro: DSCLRC
- arxiv: https://arxiv/abs/1610.01708
Deeply supervised salient object detection with short connecti
- arxiv: https://arxiv/abs/1611.04849
Weakly Supervised Top-down Salient Object Detection
- intro: anyang Technological University
- arxiv: https://arxiv/abs/1611.0545
SalGA: Visual Saliency Prediction with Generative Adversarial etworks
- project page: https://imatge-upc.github.io/saliency-salgan-2017/
- arxiv: https://arxiv/abs/1701.01081
Visual Saliency Prediction Using a Mixture of Deep eural etworks
- arxiv: https://arxiv/abs/1702.0072
A Fast and Compact Salient Score Regression etwork Based on Fully Convolutional etwork
- arxiv: https://arxiv/abs/1702.00615
Saliency Detection by Forward and Backward Cues in Deep-Cs
https://arxiv/abs/170.00152
Supervised Adversarial etworks for Image Saliency Detection
https://arxiv/abs/1704.07242
Saliency Detection in Video
Deep Learning For Video Saliency Detection
- arxiv: https://arxiv/abs/1702.00871
Visual Relatihip Detection
Visual Relatihip Detection with Language Priors
- intro: ECCV 2016 oral
- paper: https://cs./people/ranjaykrishna/vrd/vrd.pdf
- github: https://github/Prof-Lu-Cewu/Visual-Relatihip-Detection
ViP-C: A Visual Phrase Reasoning Convolutional eural etwork for Visual Relatihip Detection
- intro: Visual Phrase reasoning Convolutional eural etwork (ViP-C), Visual Phrase Reasoning Structure (VPRS)
- arxiv: https://arxiv/abs/1702.07191
Visual Translation Embedding etwork for Visual Relation Detection
- arxiv: https://www.arxiv/abs/1702.0819
Deep Variation-structured Reinforcement Learning for Visual Relatihip and Attribute Detection
- intro: CVPR 2017 spotlight paper
- arxiv: https://arxiv/abs/170.0054
Detecting Visual Relatihips with Deep Relational etworks
- intro: CVPR 2017 oral. The Chinese University of Hong Kong
- arxiv: https://arxiv/abs/1704.0114
Specific Object Deteciton
Face Deteciton
Multi-view Face Detection Using Deep Convolutional eural etworks
- intro: Yahoo
- arxiv: http://arxiv/abs/1502.02766
- github: https://github/guoyilin/FaceDetection_C
From Facial Parts Respes to Face Detection: A Deep Learning Approach
- project page: http://personal.hk/~ys014/projects/Faceness/Faceness.html
Compact Convolutional eural etwork Cascade for Face Detection
- arxiv: http://arxiv/abs/1508.01292
- github: https://github/Bkmz21/FD-Evaluation
Face Detection with End-to-End Integration of a Convet and a D Model
- intro: ECCV 2016
- arxiv: https://arxiv/abs/1606.00850
- github(MXet): https://github/tfwu/FaceDetection-Convet-D
CMS-RC: Contextual Multi-Scale Region-based C for Unctrained Face Detection
- intro: CMU
- arxiv: https://arxiv/abs/1606.0541
Finding Tiny Faces
- intro: CMU
- project page: http://cmu.edu/~peiyunh/tiny/index.html
- arxiv: https://arxiv/abs/1612.04402
- github: https://github/peiyunh/tiny
Towards a Deep Learning Framework for Unctrained Face Detection
- intro: overlap with CMS-RC
- arxiv: https://arxiv/abs/1612.0522
Supervised Transformer etwork for Efficient Face Detection
- arxiv: http://arxiv/abs/1607.05477
UnitBox: An Advanced Object Detection etwork
- intro: ACM MM 2016
- arxiv: http://arxiv/abs/1608.01471
Bootstrapping Face Detection with Hard egative Examples
- author: 万韶华 @ 小米.
- intro: Faster R-C, hard negative mining. state-of-the-art on the FDDB dataset
- arxiv: http://arxiv/abs/1608.0226
Grid Loss: Detecting Occluded Faces
- intro: ECCV 2016
- arxiv: https://arxiv/abs/1609.00129
- paper: http://lrs.at/pubs/opitz_eccv_16.pdf
- poster: http:///files/posters/P-2A-4.pdf
A Multi-Scale Cascade Fully Convolutional etwork Face Detector
- intro: ICPR 2016
- arxiv: http://arxiv/abs/1609.056
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional etworks
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional eural etworks
- project page: https://kpzhang9.github.io/MTC_face_detection_alignment/index.html
- arxiv: https://arxiv/abs/1604.02878
- github(Matlab): https://github/kpzhang9/MTC_face_detection_alignment
- github: https://github/pangyupo/mxnet_mtcnn_face_detection
- github: https://github/DaFuCoding/MTC_Caffe
- github(MXet): https://github/Seanlinx/mtcnn
- github: https://github/Pi-DeepLearning/RaspberryPi-FaceDetection-MTC-Caffe-With-Motion
- github(Caffe): https://github/foreverYoungGitHub/MTC
- github: https://github/CongWeilin/mtcnn-caffe
Face Detection using Deep Learning: An Improved Faster RC Approach
- intro: DeepIR Inc
- arxiv: https://arxiv/abs/1701.08289
Faceness-et: Face Detection through Deep Facial Part Respes
- intro: An extended version of ICCV 2015 paper
- arxiv: https://arxiv/abs/1701.089
Multi-Path Region-Based Convolutional eural etwork for Accurate Detection of Unctrained “Hard Faces”
- intro: CVPR 2017. MP-RC, MP-RP
- arxiv: https://arxiv/abs/170.09145
End-To-End Face Detection and Recognition
https://arxiv/abs/170.10818
Facial Point / Landmark Detection
Deep Convolutional etwork Cascade for Facial Point Detection
- homepage: http://mmlab.hk/archive/C_FacePoint.htm
- paper: http://hk/~xgwang/papers/sunWTcvpr1.pdf
- github: https://github/luoyetx/deep-landmark
Facial Landmark Detection by Deep Multi-task Learning
- intro: ECCV 2014
- project page: http://mmlab.hk/projects/TCDC.html
- paper: http://personal.hk/~ccloy/files/eccv_2014_deepfacealign.pdf
- github(Matlab): https://github/zhzhanp/TCDC-face-alignment
A Recurrent Encoder-Decoder etwork for Sequential Face Alignment
- intro: ECCV 2016
- arxiv: https://arxiv/abs/1608.05477
Detecting facial landmarks in the video based on a hybrid framework
- arxiv: http://arxiv/abs/1609.06441
Deep Ctrained Local Models for Facial Landmark Detection
- arxiv: https://arxiv/abs/1611.08657
Effective face landmark localization via single deep network
- arxiv: https://arxiv/abs/1702.02719
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relatihips for Single Shot Keypoint Detection
https://arxiv/abs/1704.01880
People Detection
End-to-end people detection in crowded scenes
- arxiv: http://arxiv/abs/1506.04878
- github: https://github/Russell91/reinspect
- ipn: http://nbviewer.ipython/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
Detecting People in Artwork with Cs
- intro: ECCV 2016 Workshops
- arxiv: https://arxiv/abs/1610.08871
Deep Multi-camera People Detection
- arxiv: https://arxiv/abs/1702.0459
Person Head Detection
Context-aware Cs for person head detection
- arxiv: http://arxiv/abs/1511.07917
- github: https://github/aosokin/cnn_head_detection
Pedestrian Detection
Pedestrian Detection aided by Deep Learning Semantic Tasks
- intro: CVPR 2015
- project page: http://mmlab.hk/projects/TA-C/
- paper: http://arxiv/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.hk/~pluo/pdf/tianLWTiccv15.pdf
Deep convolutional neural networks for pedestrian detection
- arxiv: http://arxiv/abs/1510.0608
- github: https://github/DenisTome/DeepPed
Scale-aware Fast R-C for Pedestrian Detection
- arxiv: https://arxiv/abs/1510.08160
ew algorithm improves speed and accuracy of pedestrian detection
- blog: http:///pub_releases/2016-02/uoc–nai020516.php
Pushing the Limits of Deep Cs 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/abs/160.04525
A Real-Time Deep Learning Pedestrian Detector for Robot avigation
- arxiv: http://arxiv/abs/1607.0446
A Real-Time Pedestrian Detector using Deep Learning for Human-Aware avigation
- arxiv: http://arxiv/abs/1607.04441
Is Faster R-C Doing Well for Pedestrian Detection?
- intro: ECCV 2016
- arxiv: http://arxiv/abs/1607.0702
- github: https://github/zhangliliang/RP_BF/tree/RP-pedestrian
Reduced Memory Region Based Deep Convolutional eural etwork Detection
- intro: IEEE 2016 ICCE-Berlin
- arxiv: http://arxiv/abs/1609.02500
Fused D: A deep neural network fusion approach to fast and robust pedestrian detection
- arxiv: https://arxiv/abs/1610.0466
Multispectral Deep eural etworks for Pedestrian Detection
- intro: BMVC 2016 oral
- arxiv: https://arxiv/abs/1611.02644
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
- intro: CVPR 2017
- project page: http://:5000/publicati/synunity/
- arxiv: https://arxiv/abs/170.0628
- github(Tensorflow): https://github/huangshiyu1/RPplus
Vehicle Detection
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
- intro: ECCV 2016
- arxiv: http://arxiv/abs/1607.04564
Evolving Boxes for fast Vehicle Detection
- arxiv: https://arxiv/abs/1702.00254
Traffic-Sign Detection
Traffic-Sign Detection and Classification in the Wild
- project page(codedataset): http:///traffic-sign/
- paper: http://120.52.7.11//openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
- code & model: http:///traffic-sign/data_model_code/newdata0411.zip
Boundary / Edge / Contour Detection
Holistically-ested Edge Detection
- intro: ICCV 2015, Marr Prize
- paper: http:///openaccess/content_iccv_2015/papers/Xie_Holistically-ested_Edge_Detection_ICCV_2015_paper.pdf
- arxiv: http://arxiv/abs/1504.0675
- github: https://github/s9xie/hed
Unsupervised Learning of Edges
- intro: CVPR 2016. Facebook AI Research
- arxiv: http://arxiv/abs/1511.04166
- zn-blog: http://www.leiphone/news/201607/b1trsg9j6GSMnjOP.html
Pushing the Boundaries of Boundary Detection using Deep Learning
- arxiv: http://arxiv/abs/1511.0786
Convolutional Oriented Boundaries
- intro: ECCV 2016
- arxiv: http://arxiv/abs/1608.02755
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
- project page: http://www./~cvlsegmentation/
- arxiv: https://arxiv/abs/1701.04658
- github: https://github/kmaninis/COB
Richer Convolutional Features for Edge Detection
- intro: richer convolutional features (RCF)
- arxiv: https://arxiv/abs/1612.0210
Skeleton Detection
Object Skeleton Extraction in atural Images by Fusing Scale-associated Deep Side Outputs
- arxiv: http://arxiv/abs/160.09446
- github: https://github/zeakey/DeepSkeleton
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in atural Images
- arxiv: http://arxiv/abs/1609.0659
SR: Side-output Residual etwork for Object Symmetry Detection in the Wild
- intro: CVPR 2017
- arxiv: https://arxiv/abs/170.0224
- github: https://github/KevinKecc/SR
Fruit Detection
Deep Fruit Detection in Orchards
- arxiv: https://arxiv/abs/1610.0677
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.au/display/AGPub/
- arxiv: https://arxiv/abs/1610.08120
Part Detection
Objects as context for part detection
https://arxiv/abs/170.09529
Others
Deep Deformation etwork for Object Landmark Localization
- arxiv: http://arxiv/abs/1605.01014
Fashion Landmark Detection in the Wild
- intro: ECCV 2016
- project page: http://personal.hk/~lz01/projects/FashionLandmarks.html
- arxiv: http://arxiv/abs/1608.0049
- github(Caffe): https://github/liuziwei7/fashion-landmarks
Deep Learning for Fast and Accurate Fashion Item Detection
- intro: Kuznech Inc.
- intro: MultiBox and Fast R-C
- paper: https:///kddfashion_finalSubmissi/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf
OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
- github: https://github/geometalab/OSMDeepOD
Selfie Detection by Synergy-Ctraint Based Convolutional eural etwork
- intro: IEEE SITIS 2016
- arxiv: https://arxiv/abs/1611.0457
Associative Embedding:End-to-End Learning for Joint Detection and Grouping
- arxiv: https://arxiv/abs/1611.05424
Deep Cuboid Detection: Beyond 2D Bounding Boxes
- intro: CMU & Magic Leap
- arxiv: https://arxiv/abs/1611.10010
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
- arxiv: https://arxiv/abs/1612.0019
Deep Learning Logo Detection with Data Expansion by Synthesising Context
- arxiv: https://arxiv/abs/1612.0922
Pixel-wise Ear Detection with Convolutional Encoder-Decoder etworks
- arxiv: https://arxiv/abs/1702.0007
Automatic Handgun Detection Alarm in Videos Using Deep Learning
- arxiv: https://arxiv/abs/1702.05147
- results: https://github/SihamTabik/Pistol-Detection-in-Videos
Object Proposal
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
- arxiv: http://arxiv/abs/1510.04445
- github: https://github/aghodrati/deepproposal
Scale-aware Pixel-wise Object Proposal etworks
- intro: IEEE Transacti on Image Processing
- arxiv: http://arxiv/abs/1601.04798
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
- intro: BMVC 2016. Attractioet
- arxiv: https://arxiv/abs/1606.04446
- github: https://github/gidariss/Attractioet
Learning to Segment Object Proposals via Recursive eural etworks
- arxiv: https://arxiv/abs/1612.01057
Learning Detection with Diverse Proposals
- intro: CVPR 2017
- keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
- arxiv: https://arxiv/abs/1704.05
Scaleet: Guiding Object Proposal Generation in Supermarkets and Beyond
- keywords: product detection
- arxiv: https://arxiv/abs/1704.06752
Improving Small Object Proposals for Company Logo Detection
- intro: ICMR 2017
- arxiv: https://arxiv/abs/1704.08881
Localization
Beyond Bounding Boxes: Precise Localization of Objects in Images
- intro: PhD Thesis
- homepage: http:///Pubs/TechRpts/2015/EECS-2015-19.html
- phd-thesis: http:///Pubs/TechRpts/2015/EECS-2015-19.pdf
- github(“SDS using hypercolumns”): https://github/bharath272/sds
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
- arxiv: http://arxiv/abs/150.00949
Weakly Supervised Object Localization Using Size Estimates
- arxiv: http://arxiv/abs/1608.0414
Active Object Localization with Deep Reinforcement Learning
- intro: ICCV 2015
- keywords: Markov Decision Process
- arxiv: https://arxiv/abs/1511.06015
Localizing objects using referring expressi
- intro: ECCV 2016
- keywords: LSTM, multiple instance learning (MIL)
- paper: http://www.umiacs./~varun/files/refexp-ECCV16.pdf
- github: https://github/varun-nagaraja/referring-expressi
Locet: Improving Localization Accuracy for Object Detection
- arxiv: http://arxiv/abs/1511.0776
- github: https://github/gidariss/Locet
Learning Deep Features for Discriminative Localization
- homepage: http:///
- arxiv: http://arxiv/abs/1512.04150
- github(Tensorflow): https://github/jazzsaxmafia/Weakly_detector
- github: https://github/metalbubble/CAM
- github: https://github/tdeboissiere/VGG16CAM-keras
ContextLocet: Context-Aware Deep etwork Models for Weakly Supervised Localization
- intro: ECCV 2016
- project page: http://www.fr/willow/research/contextlocnet/
- arxiv: http://arxiv/abs/1609.041
- github: https://github/vadimkantorov/contextlocnet
Tutorials / Talks
Convolutional Feature Maps: Elements of efficient (and accurate) C-based object detection
- slides: http:///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/challenges/talks/2016/Hikvision_at_Imageet_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 GoogLeet-OverFeat algorithm. We additionally provide an implementation of theReInspect algorithm”
- github: https://github/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
- github: https://github/fmassa/
Using DIGITS to train an Object Detection network
- github: https://github/VIDIA/DIGITS/blob/master/examples/object-detection/
FC-MultiBox Detector
- intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
- github: https://github/teaonly/
KittiBox: A car detection model implemented in Tensorflow.
- keywords: Multiet
- intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
- github: https://github/MarvinTeichmann/KittiBox
Tools
BeaverDam: Video annotation tool for deep learning training labels
https://github/antingshen/BeaverDam
Blogs
Convolutional eural etworks for Object Detection
http://rnd.azoft/convolutional-neural-networks-object-detection/
Introducing automatic object detection to visual search (Pinterest)
- keywords: Faster R-C
- blog: https://engineering.pinterest/blog/introducing-automatic-object-detection-visual-search
- demo: https://engineering.pinterest/sites/engineering/files/Visual%20Search%20V1%20-%
- review: https://news./pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlBSWlRVXJXb1MrQ01TDRIMGxLQWlBczFIeWg0TFRUdn2UHY2ZWFiXC9QQVwvQzBHMB0UzBZblpOSmUyU1FcLzPWXI4cml2VERwTTJsOFwvOEk9In0%D
Deep Learning for Object Detection with DIGITS
- blog: https:///parallelforall/deep-learning-object-detection-digits/
Analyzing The Papers Behind Facebook’s Computer Vision Approach
- keywords: DeepMask, SharpMask, MultiPathet
- blog: http://blog.dlib/2016/10/easily-create-high-quality-object.html
How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
- blog: https:///machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
- github: https://github/Microsoft/CTK/tree/master/Examples/Image/Detection/FastRC
Object Detection in Satellite Imagery, a Low Overhead Approach
- part 1: https://medium/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
- part 2: https://medium/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-89f40122f92#.f9b7dgf64
You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional eural etworks
- part 1: https://medium/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-8dad1cf7571#.fmmi2oof
- part 2: https://medium/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-4f72f659588#.nwzarsz1t
Faster R-C Pedestrian and Car Detection
- blog: https://bigsnarf.wordpress/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
- ipn: https://gist.github/bigsnarfdude/2f7b2144065f6056892a98495644de0#file-demo_faster_rcnn_notebook-ipynb
- github: https://github/bigsnarfdude/Faster-RC_TF
Small U-et for vehicle detection
- blog: https://medium/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad
Region of interest pooling explained
- blog: https://deepsense.io/region-of-interest-pooling-explained/
- github: https://github/deepsense-io/roi-pooling
Deep Learning（深度学习）：
ufldl的2个教程(这个没得说，入门绝对的好教程，g的，逻辑清晰有练习)：一
ufldl的2个教程(这个没得说，入门绝对的好教程，g的，逻辑清晰有练习)：二
Bengio团队的deep learning教程，用的theano库，主要是rbm系列，搞python的可以参考，很不错。
deeplearning主页，里面包含的信息量非常多，有software, reading list, research lab, dataset, demo等，强烈推荐，自己去发现好资料。
Deep learning的toolbox，matlab实现的，对应源码来学习一些常见的DL模型很有帮助，这个库我主要是用来学习算法实现过程的。
201年龙星计划深度学习教程，邓力大牛主讲，虽然老师准备得不充分，不过还是很有收获的。
Hinton大牛在coursera上开的神经网络课程，DL部分有不少，非常赞，没有废话，课件每句话都包含了很多信息，有一定DL基础后去听收获更大。
Larochelle关于DL的课件，逻辑清晰，覆盖面广,包含了rbm系列，autoencoder系列，sparse coding系列，还有crf，cnn，rnn等。虽然网页是法文，但是课件是英文。
CMU大学201年的deep learning课程，有不少reading paper可以参考。
达慕思大学Lorenzo Torresani的201Deep learning课程reading list.
Deep Learning Methods for Vision(余凯等在cvpr2012上组织一个workshop，关于DL在视觉上的应用)。
斯坦福g团队成员链接主页，可以进入团队成员的主页，比较熟悉的有Richard Socher, Honglak Lee, Quoc Le等。
多伦多ML团队成员链接主页，可以进入团队成员主页，包括DL鼻祖hinton，还有Ruslan Salakhutdinov , Alex Krizhevsky等。
蒙特利尔大学机器学习团队成员链接主页，包括大牛Bengio，还有Ian Goodfellow 等。
纽约大学的机器学习团队成员链接主页，包括大牛Lecun，还有Rob Fergus等。
Charlie Tang个人主页，结合DLSVM.
豆瓣上的脑与deep learning读书会，有讲义和部分视频，主要介绍了一些于deep learning相关的生物神经网络。
Large Scale ML的课程，由Lecun和Langford讲的，能不推荐么。
Yann Lecun的2014年Deep Learning课程主页。 视频链接。
吴立德老师《深度学习课程》
一些常见的DL code列表，csdn博主zouxy09的博文，Deep Learning源代码收集-持续更新…
Deep Learning for LP (without Magic)，由DL界5大高手之一的Richard Socher小组搞的，他主要是LP的。
2012 Graduate Summer School: Deep Learning, Feature Learning，高手云集，深度学习盛宴，几乎所有的DL大牛都有参加。
matlab下的maxPooling速度优化，调用C实现的。
2014年ACL机器学习领域主席Kevin Duh的深度学习入门讲座视频。
R-C code: Regi with Convolutional eural etwork Features.
Machine Learning（机器学习）：
介绍图模型的一个ppt，非常的赞，ppt作者总结得很给力，里面还包括了HMM，MEM, CRF等其它图模型。反正看完挺有收获的。
机器学习一个视频教程，youtube上的，翻吧，内容很全面，偏概率统计模型，每一小集只有几分钟。
龙星计划2012机器学习，由余凯和张潼主讲。
demtrate 的 blog :关于PGM(概率图模型)系列，主要按照Daphne Koller的经典PGM教程介绍的，大家依次google之。
FreeMind的博客，主要关于机器学习的。
Tom Mitchell大牛的机器学习课程，他的machine learning教科书非常出名。
CS109,Data Science,用python介绍机器学习算法的课程。
CCF主办的一些视频讲座。
国外技术团队博客：
etflix技术博客,很多干货。
Computer Vision（计算机视觉）：
MIT201年秋季课程：Advances in Computer Vision，有练习题，有些有code.
IPAM一个计算机视觉的短期课程，有不少牛人参加。
OpenCV相关：
http://opencv/
2012年7月4日随着opencv2.4.2版本的发布，opencv更改了其最。
http:///
好像12年才有这个论坛的，比较新。里面有针对《learning opencv》这本书的视频讲解，不过视频教学还没出完，正在更新中。对刚入门学习opencv的人来说很不错。
http:///forum/
opencv中文论坛，对于初次接触opencv的学者来说比较不错，入门资料多，opencv的各种英文文档也翻译成中文了。不足是感觉这个论坛上发帖提问很少人回答，也就是说讨论不够激烈。
http://opencv.jp/
opencv的日文网站，里面有不少例子代码，看不懂日文可以用网站自带的翻译，能看个大概。
http:///projects/opencv
opencv版本bug修补，版本更新，以及各种相关大型活动安排,还包含了opencv最近几个月内的活动路线，即未来将增加的功能等，可以掌握各种关于opencv进展情况的最新进展。
http://tech.groups.yahoo/group/OpenCV/
opencv雅虎邮件列表，据说是最好的opencv论坛，信息更新最新的地方。不过个人认为要查相关主题的内容，在邮件列表中非常不方便。
http:///~jsyeh/wiki/doku.php
台湾大学暑假集训网站，内有链接到与opencv集训相关的网页。感觉这种教育形式还蛮不错的。
http://sourceforge/projects/opencvlibrary/
opencv版本发布地方。
http:///projects/opencv/wiki/ChangeLog#241 http://opencv.willowgarage/wiki/OpenCV%20Change%20Logs
opencv版本内容更改日志网页,前面那个网页更新最快。
http:///opencvdoc/2..2/html/doc/tutorials/tutorials.html
opencv中文教程网页，分几个模块讲解，有代码有过程。内容是网友翻译opencv自带的doc文件里的。
https://netfiles./jbhuang1/www/resources/vision/index.html
网友总结的常用带有cvpr领域常见算法code链接的网址，感觉非常的不错。
http://fossies/dox/OpenCV-2.4.2/
该网站可以查看opencv中一些函数的变量接口，还会列出函数之间的结构图。
http://opencv.itseez/
opencv的函数、类等查网页，有导航，查起来感觉不错。
优化：
submodual优化网页。
Geoff Gordon的优化课程，youtube上有对应视频。
数学：
http://www.youku/playlist_show/id_19465801.html
《计算机中的数学》系列视频，8位老师10讲内容，生动介绍微积分和线性代数基本概念在计算机学科中的各种有趣应用！
Linux学习资料：
http://itercast/library/1
linux入门的基础视频教程，对于新手可选择看第一部分，视频来源于LinuxCast网站，还不错。
OpenIKinect相关：
http://1.yuhuazou.sinaapp/
网友晨宇思远的博客，主攻cvpr，ai等。
http:///chenli2010/article/details/6887646
kinect和openni学习资料汇总。
http:///moc062066/article/category/871261
OpenCV 计算机视觉 kinect的博客:
http://kheresy.wordpress/index_of_openni_and_kinect/comment-page-5/
网友Heresy的博客，里面有不少kinect的文章，写的比较详细。
http://wwwkinect/
体感游戏中文网，有不少新的kinect资讯。
http://www.kinectutorial/
Kinect体感开发网。
http://code.google/p/openni-hand-tracker
openni_hand_tracking google code项目。
http:///
网友的kinect博客，里面有很多手势识别方面的文章介绍，还有源码，不过貌似是基于c#的。
https://sites.google/site/colordepthfusion/
一些关于深度信息和颜信息融合（fusion）的文章。
http://projects.ict./mxr/faast/
kinect新的库，可以结合OpenI使用。
https://sites.google/a/chalearn/gesturechallenge/
kinect手势识别网站。
http://www.ros/wiki/mit-ros-pkg
mit的kinect项目，有code。主要是与手势识别相关。
http://uk/blog/2012/08/kinecting-people-our-top-6-kinect-projects/
kinect 2012年度最具创新的6个项目，有视频，确实够创新的！
http://wwwblogs/yangyangcv/archive/2011/01/07/19049.html
kinect多点触控的一篇博文。
http://sourceforge/projects/kinect-mex/
http:///matlabcentral/fileexchange/0242-kinect-matlab
有关matlab for kinect的一些接口。
http://news.9ria/2012/1212/25609.html
AIR和Kinect的结合，有一些手指跟踪的code。
http://sg/computervision/people/home/renzhou/index.htm
研究kinect手势识别的，任洲。刚毕业不久。
其他网友cvpr领域的链接总结：
http://wwwblogs/kshenf/
网友整理常用牛人链接总结，非常多。不过个人没有没有每个网站都去试过。所以本文也是我自己总结自己曾经用过的或体会过的。
OpenGL有关:
http://nehe.gamedev/
eHe的OpenGL教程英文版。
http:///DancingWind/
eHe的OpenGL教程对应的中文版，由网友周玮翻译的。
http://www.qiliang/old/nehe_qt/
eHe的OpengGL对应的Qt版中文教程。
http:///qp120291570
网友左脑设计，右脑编程的Qt_OpenGL博客,写得还不错。
http://guiliblearning.blogspot/
这个博客对opengl的机制有所剖析，貌似要FQ才能进去。
cvpr综合网站论坛博客等：
http:///
中国计算机视觉论坛
http://.info/
这个博客很不错，每次看完都能让人兴奋，因为有很多关于cv领域的科技新闻，还时不时有视频显示。另外这个博客里面的资源也整理得相当不错。中文的。
http://www.bfcat/
一位网友的个人计算机视觉博客，有很多关于计算机视觉前沿的东西介绍，与上面的博客一样，看了也能让人兴奋。
http:///v_JULY_v/
牛人博客，主攻数据结构，机器学习数据挖掘算法等。
http://blog.youtueye/
该网友上面有一些计算机视觉方向的博客,博客中附有一些实验的测试代码.
http://blog.sciencenet/u/jingyanwang
多看pami才扯谈的博客，其中有不少pami文章的中文介绍。
http:///
做网络和自然语言处理的，有不少机器学习方面的介绍。
ML常用博客资料等：
http://freemind.pluskid/
由 pluskid 所维护的 blog，主要记录一些机器学习、程序设计以及各种技术和非技术的相关内容，写得很不错。
http://datasciencemasters/
里面包含学ML/DM所需要的一些知识链接，且有些给出了视频教程，网页资料，电子书，开源code等，推荐！
http:///zhouzh/index.htm
周志华主页，不用介绍了，机器学习大牛，更可贵的是他的很多文章都有源码公布。
http:///~jpaisley/Papers.htm
John Paisley的个人主页，主要研究机器学习领域，有些文章有代码提供。
http://foreveralbum.yo2/
里面有一些常见机器学习算法的详细推导过程。
http:///abcjennifer
浙江大学CS硕士在读，关注计算机视觉，机器学习，算法研究，博弈， 人工智能， 移动互联网等学科和产业。该博客中有很多机器学习算法方面的介绍。
http://www.wytk2008/
无垠天空的机器学习博客。
http:///index.html
机器学习挑战赛。
http://licstar/
licstar的技术博客，偏自然语言处理方向。
国内科研团队和牛人网页：
http://vision.ia.ac/zh/index_cn.html
中科院自动化所机器视觉课题小组，有相关数据库、论文、课件等下载。
http://ia.ac/users/szli/
李子青教授个人主页，中科院自动化所cvpr领域牛叉人！
http://www4p.hk/~cslzhang/
香港理工大学教授lei zhang个人主页，也是cvpr领域一大牛人啊，cvpr，iccv各种发表。更重要的是他所以牛叉论文的code全部公开，非常难得！
http://liama.ia.ac/wiki/start
中法信息、自动化与应用联合实验室，里面很多内容不仅限而cvpr，还有ai领域一些其他的研究。
http:///cvl/english/
厦门大学特聘教授，cv领域一位牛人。研究方向主要为目标检测，目标跟踪，运动估计，三维重建，鲁棒统计学，光流计算等。
http://idm./index.aspx
北京大学数字视频编码技术国家实验室。
http:///~cjlin/libsvm/
libsvm项目网址，台湾大学的，很火！
http://www.jdl.ac/user/sgshan/index.htm
山世光，人脸识别研究比较牛。在中国科学院智能信息处理重点实验室
国外科研团队和牛人网页：
https://netfiles./jbhuang1/www/resources/vision/index.html
常见计算机视觉资源整理索引，国外学者整理，全是出名的算法，并且带有代码的，这个非常有帮助，其链接都是相关领域很火的代码。
http://cmu.edu/afs/cs/project/cil/ftp/html/txtv-groups.html
国外学者整理的各高校研究所团队网站
http:///en-us/groups/vision/
微软视觉研究小组，不解释，大家懂的，牛！
http://lear.inrialpes.fr/index.php
法国国家信息与自动化研究所，有对应牛人的链接，论文项目网页链接，且一些code对应链接等。
http:///~pcarbo/objrecls/
Learning to recognize objects with little supervision该篇论文的项目网页，有对应的code下载，另附有详细说明。
http:///~lbourdev/poselets/
poselets相关研究界面，关于poselets的第一手资料。
http://fi/CMV/Research
芬兰奥卢大学计算机科学与工程学院网页，里面有很多cv领域相关的研究，比如说人脸，脸部表情，行为识别，跟踪，人机交互等cv基本都涉及有。
http://cmu.edu/~cil/vision.html
卡耐基梅隆大学计算机视觉主页，内容非常多。可惜的是该网站内容只更新到了2004年。
http://vision./index.html
斯坦福大学计算机视觉主页，里面有非常非常多的牛人，比如说大家熟悉的lifeifei.
http://www.wavelet/index.php
关于wavelet研究的网页。
http://civs./
加州大学洛杉矶分校统计学院，关于统计学习方面各种资料，且有相应的网上公开课。
http://cmu.edu/~efros/
卡耐基梅隆大学Alexei(Alyosha)Efros教授个人网站，计算机图形学高手。
http:///torralba/www//
mit牛人Associate教授个人网址，主要研究计算机视觉视觉感知，目标识别和场景理解等。
http:///billf/
mit牛人William T. Freeman教授，主要研究计算机视觉和图像学
http://www.research.ibm/peoplevision/
IBM视觉研究中心，里面除了有其研究小组的最新成果外，还有很多测试数据(特别是视频）供下载。
http://www.vlfeat/
vlfeat主页，vlfeat也是一个开源组织，主要定位在一些最流行的视觉算法开源上，编写，其很多算法效果比opencv要好,不过数量不全，但是非常有用。
http://www.ac.uk/~az/
Andrew Zisserman的个人主页，这人大家应该熟悉，《计算机视觉中的多视几何》这本神书的作者之一。
http:///~grauman/
KristenGrauman教授的个人主页，是个大美女，且是2011年“马尔奖”获得者，”马尔奖“大家都懂的，计算机视觉领域的最高奖项，目前无一个国内学者获得过。她的主要研究方法是视觉识别。
http:///vision/welcome/
mit视觉实验室主页。
http://code.google/p/sixthsense/
曾经在网络上非常出名一个视频，一个作者研究的第六感装置，现在这个就是其开源的主页。
http://vision./~pdollar/research.html#BehaviorRecognitionAnimalBehavior
Piotr Dollar的个人主要，主要研究方向是行为识别。
http://rwth-aachen.de/
移动多媒体处理，将移动设备，计算机图像学，视觉，图像处理等结合的领域。
http://www.fr/~laptev/index.html
Ivan Laptev牛人主页，主要研究行为识别。有很多数据库可以下载。
http:///hess/
Rob Hess的个人主要，里面有源码下载，比如说粒子滤波，他写的粒子滤波在网上很火。
http://morethantechnical.googlecode/svn/trunk/
cvpr领域一些小型的开源代码。
http://iica.de/pd/index.py
做行人检测的一个团队，内部有一些行人检测的代码下载。
http:///~grauman/research/pubs.html
UT-Austin计算机视觉小组，包含的视觉研究方向比较广，且有的文章有源码，你只需要填一个邮箱地址，系统会自动发跟源码相关的信息过来。
http://www.ac.uk/~vgg/index.html
visual geometry group
图像:
http://blog.sina/s/blog_4cccd8d01012pw5.html
交互式图像分割代码。
http:///code/
graphcut优化代码。
语音：
http://danielpovey/kaldi-lectures.html
语音处理中的kaldi学习。
算法分析与设计（计算机领域的基础算法）：
http://www.51nod/focus.html
该网站主要是讨论一些算法题。里面的李陶冶是个大牛，回答了很多算法题。
一些综合topic列表：
http://cornell.edu/courses/CS7670/2011fa/
计算机视觉中的些topic（Special Topics in Computer Vision），截止到2011年为止，其引用的文章都是非常顶级的topic。
书籍相关网页：
http://www.imageprocessingplace/index.htm
冈萨雷斯的《数字图像处理》一书网站，包含课程材料，matlab图像处理工具包，课件ppt等相关素材。
Cumer Depth Cameras for Computer Vision
很优秀的一本书，不过很贵，买不起啊！做深度信息的使用这本书还不错，google图中可以预览一部分。
Making.Things.See
针对Kinect写的，主要关注深度信息，较为基础。书籍中有不少例子，貌似是java写的。
国内一些AI相关的研讨会：
http://www.iipl./MLA1/index.htm
中国机器学习及应用研讨会(这个是201年的)
期刊会议论文下载：
http://cvpapers/
几个顶级会议论文公开下载界面，比如说ICCV,CVPR,ECCV,ACCV,ICPR,SIGGRAPH等。
http:///
cvpr2012的官方地址，里面有各种资料和信息，其他年份的地址类似推理更改即可。
http://www.sciencedirect/science/journal/02628856
ICV期刊下载
http://wwwputer/portal/web/tpami
TPAMI期刊，AI领域中可以算得上是最顶级的期刊了，里面有不少cvpr方面的内容。
http://www.springerlink/content/100272/
IJCV的网址。
http:///
IPS，有论文下载列表。
http://graphlab/lsrs201/program/
LSRS （会议）地址，大规模推荐系统，其它年份依次类推。
会议期刊相关信息：
http://conferences.visionbib/Iris-Conferences.html
该网页列出了图像处理，计算机视觉领域相关几乎所有比较出名的会议时间表。
http://conferences.visionbib/Browse-conf.php
上面网页的一个子网页，列出了最近的CV领域提交paper的deadline。
cvpr相关数据库下载：
http:///en-us/um/people/jckrumm/WallFlower/TestImages.htm
微软研究院牛人Wallflower Paper的论文中用到的目标检测等测试图片
http://archive.ics./ml/
UCI数据库列表下载，最常用的机器学习数据库列表。
http:///~rmessing/uradl/
行为识别通过关键点的跟踪视频数据库，Rochester university的
http://www.research.ibm/peoplevision/performanceevaluation.html
IBM视觉研究中心，有视频监控等非常多的测试视频。
http:///datasets.html
该网站上列出了常见的cvpr研究的数据库。
http:///rgbd-dataset/index.html
RGB-D Object Dataset.做目标识别的。
AI相关页：
http://en.akinator/
该网站很好玩，可以测试你心里想出的一个人名(当然前提是这个人必须有一定的知名度)，然后该网站会提出一系列的问题，你可以选择yes or no,or I don’t know等等，最后系统会显示你心中所想的那个人。
http://www./
人与狗的匹配游戏，摄像头采集人脸，呵呵…
Android相关：
https://code.google/p/android-ui-utils/
该网站上有一些android图标,菜单等跟界面有关的设计工具,可以用来做一些简单的UI设计.
工具和code下载：
http://lear.inrialpes.fr/people/dorko/downloads.html
6种常见的图像特征点检测子，linux下环境运行。不过只提供了二进制文件，不提供源码。
http:///~pcarbo/objrecls/index.html#code
ssmcmc的matlab代码,是Learning to recognize objects with little supervision这一系列文章用的源码，属于目标识别方面的研究。
http://www.ac.uk/~timork/
仿射无关尺度特征点检测算子源码，还有些其它算子的源码或二进制文件。
http://www./~bleibe/code/ism.html
隐式形状模型(ISM)项目主页，作者Bastian Leibe提供了linux下运行的二进制文件。
http://www.fr/~laptev/download.html#stip
Ivan Laptev牛人主页中的STIP特征点检测code，但是也只是有二进制文件，无源码。该特征点在行为识别中该特征点非常有名。
http://ai./~quocle/
斯坦福大学Quoc V.Le主页，上有它2011年行为识别文章的代码。
开源软件：
http://mloss/software/
一些ML开源软件在这里基本都可以搜到，有上百个。
https://github/myui/hivemall
Scalable machine learning library for Hive/Hadoop.
http://scikit-learn/stable/
基于python的机器学习开源软件，文档写得不错。
挑战赛：
http://in/kaggle-competition-soluti/
kaggle一些挑战赛的code.
公开课：
网易公开课，国内做得很不错的公开课，翻译了一些国外出名的公开课教程，与国外公开课平台coursera有合作。
coursera在线教育网上公开课，很新，有个邮箱注册即可学习，有不少课程，且有对应的练习，特别是编程练习，超赞。
斯坦福网上公开课链接，有统计学习，凸优化等课程。
udacity公开课程下载链接，其实速度还可以。里面有不少好教程。
机器学习公开课的连接，有不少课。
在最近的学习中，看到一些有用的资源就记下来了，现在总结一下，欢迎补充！
机器视觉开源代码合集
计算机视觉算法与代码集锦
计算机视觉的一些测试数据集和源码站点
SIFT
SURF PCA-SIFT and SIFT 开源代码 总结
常用图像数据集：标注、检索
KTH-TIPS2 image dataset
视频中行为识别公开数据库汇总
MSR Action Recognition Datasets and Codes
Sparse coding simulation software
稀疏表示
Deep Learning源代码收集-持续更新
Training a deep autoencoder or a classifier on MIST digits
Charlie Tang
本文实现了09年CVPR的文章
Kaggle 机器学习竞赛冠军及优胜者的源代码汇总
Feature_detection
机器学习视频公开课
机器学习的最佳入门学习资源
http://blog.jobbole/8260/
国外程序员整理的机器学习资源大全
一些下载资源的链接
Some Useful Links
A Library for Large Linear Classification
本博文转自
http:///huixingshao/article/details/71406084
https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#t-cnn
本人常用资源整理(ing...)
作者：好记性不如烂笔头！
出处：http://wwwblogs/zlslch/
本文版权归作者和博客园共有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文链接，否则保留追究法律责任的权利。
转载于:https://wwwblogs/leoking01/p/7227852.html
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