機器學習和深度學習資源彙總（陸續更新）

 

 

 

　　

　　很少說，直接上乾貨！

 

 

    本篇博客的目地，是對工做學習過程當中所遇所見的一些有關深度學習、機器學習的優質資源，做分類彙總，方便本身查閱，也方便他人學習借用。

    主要會涉及一些優質的理論書籍和論文、一些實惠好用的工具庫和開源庫、一些供入門該理論入門所用的demo等等。

    因爲本博客將不按期更新，儘可能將較爲前沿的深度學習、機器學習內容整理下來，須要轉載的同窗儘可能附上本文的連接，方便得到最新的內容。

 

 

 

機器學習領域相關的大牛推薦（陸續更新）

 

 

• 相關的理論、書籍、論文、課程、博客：

• [Book] Yoshua Bengio, Ian Goodfellow, Aaron Courville. Deep Learning. 2015.

• [Book] Michael Nielsen. Neural Networks and Deep Learning. 2015.
• [Course] Convolutional Neural Networks for Visual Recognition. 2015

• [Course] Deep Learning for Natural Language Processing. 2015.

• [Blog] Hacker’s Guide to Neural Networks.
• [Book] Notes on Convolutional Neural Networks
• [Book] A guide to convolution arithmetic for deep learning

 

 

 

• 相關的庫、工具

• Theano (Python)

• Libraries based on Theano: Lasagne, Keras, Pylearn2

• Caffe (C++, with Python wrapper)

• TensorFlow (Python, C++)

• Torch (Lua)

• ConvNetJS (Javascript)

• Deeplearning4j (Java)

• MatConvNet (Matlab)
• Spark machine learning library(Java,scala,python)
• LIBSVM A Library for Support Vector Machines(C/C++,Java and other languages)

 

 

• 相關的開源項目、demo

• Facial Keypoint Detection

• Deep Dream

• Eyescream

• Deep Q-network (Atari game player)

• Caffe to Theano Model Conversion (use Caffe pretrained model in Lasagne)

• R-CNN

• Fast R-CNN

 

 

 

 

 

 

 

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)	77.2%		75.8%		25.1%(@[0.5-0.95]), 43.1%(@0.5)	46 fps
SSD512 (VGG16)	79.8%		78.5%		28.8%(@[0.5-0.95]), 48.5%(@0.5)	19 fps
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	89.8%		84.2%			750ms(CPU), 46ms(TitianX)

 

 

Leaderboard

Detection Results: VOC2012

• intro: Competition 「comp4」 (train on additional data)
• homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

 

 

Papers

Deep Neural Networks for Object Detection

• paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• arxiv: http://arxiv.org/abs/1312.6229
• github: https://github.com/sermanet/OverFeat
• code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

 

 

 

 

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv: http://arxiv.org/abs/1311.2524
• supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
• slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
• slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
• github: https://github.com/rbgirshick/rcnn
• notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
• caffe-pr(「Make R-CNN the Caffe detection example」): https://github.com/BVLC/caffe/pull/482

 

 

MultiBox

Scalable Object Detection using Deep Neural Networks

• intro: first MultiBox. Train a CNN to predict Region of Interest.
• arxiv: http://arxiv.org/abs/1312.2249
• github: https://github.com/google/multibox
• blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html

Scalable, High-Quality Object Detection

• intro: second MultiBox
• arxiv: http://arxiv.org/abs/1412.1441
• github: https://github.com/google/multibox

 

 

 

 

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• intro: ECCV 2014 / TPAMI 2015
• arxiv: http://arxiv.org/abs/1406.4729
• github: https://github.com/ShaoqingRen/SPP_net
• notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/

 

 

 

DeepID-Net

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

• intro: PAMI 2016
• intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
• project page: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
• arxiv: http://arxiv.org/abs/1412.5661

Object Detectors Emerge in Deep Scene CNNs

• arxiv: http://arxiv.org/abs/1412.6856
• paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
• paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
• slides: http://places.csail.mit.edu/slide_iclr2015.pdf

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

• intro: CVPR 2015
• project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
• arxiv: https://arxiv.org/abs/1502.04275
• github: https://github.com/YknZhu/segDeepM

 

 

 

 

 

NoC

Object Detection Networks on Convolutional Feature Maps

• intro: TPAMI 2015
• arxiv: http://arxiv.org/abs/1504.06066

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

• arxiv: http://arxiv.org/abs/1504.03293
• slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
• github: https://github.com/YutingZhang/fgs-obj

 

 

 

 

Fast R-CNN

Fast R-CNN

• arxiv: http://arxiv.org/abs/1504.08083
• slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
• github: https://github.com/rbgirshick/fast-rcnn
• github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
• webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
• notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
• notes: http://blog.csdn.net/linj_m/article/details/48930179
• github(「Fast R-CNN in MXNet」): https://github.com/precedenceguo/mx-rcnn
• github: https://github.com/mahyarnajibi/fast-rcnn-torch
• github: https://github.com/apple2373/chainer-simple-fast-rnn
• github(Tensorflow): https://github.com/zplizzi/tensorflow-fast-rcnn

 

 

 

 

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

• arxiv: http://arxiv.org/abs/1505.02146
• github: https://github.com/weichengkuo/DeepBox

 

 

 

 

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model

• intro: ICCV 2015. MR-CNN
• arxiv: http://arxiv.org/abs/1505.01749
• github: https://github.com/gidariss/mrcnn-object-detection
• notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
• notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
• my notes: Who can tell me why there are a bunch of duplicated sentences in section 7.2 「Detection error analysis」? :-D

 

 

 

 

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1506.01497
• gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
• slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
• github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
• github: https://github.com/rbgirshick/py-faster-rcnn
• github: https://github.com/mitmul/chainer-faster-rcnn
• github: https://github.com/andreaskoepf/faster-rcnn.torch
• github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
• github: https://github.com/smallcorgi/Faster-RCNN_TF
• github: https://github.com/CharlesShang/TFFRCNN
• github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
• github: https://github.com/yhenon/keras-frcnn

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

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

Contextual Priming and Feedback for Faster R-CNN

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

An Implementation of Faster RCNN with Study for Region Sampling

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

 

 

 

 

 

 

YOLO

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

• arxiv: http://arxiv.org/abs/1506.02640
• code: http://pjreddie.com/darknet/yolo/
• github: https://github.com/pjreddie/darknet
• blog: https://pjreddie.com/publications/yolo/
• slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
• reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
• github: https://github.com/gliese581gg/YOLO_tensorflow
• github: https://github.com/xingwangsfu/caffe-yolo
• github: https://github.com/frankzhangrui/Darknet-Yolo
• github: https://github.com/BriSkyHekun/py-darknet-yolo
• github: https://github.com/tommy-qichang/yolo.torch
• github: https://github.com/frischzenger/yolo-windows
• gtihub: https://github.com/AlexeyAB/yolo-windows

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

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

Start Training YOLO with Our Own Data

• intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
• blog: http://guanghan.info/blog/en/my-works/train-yolo/
• github: https://github.com/Guanghan/darknet

R-CNN minus R

• arxiv: http://arxiv.org/abs/1506.06981

 

 

AttentionNet

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

• intro: ICCV 2015
• intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
• arxiv: http://arxiv.org/abs/1506.07704
• slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
• slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf

 

 

 

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

• arxiv: http://arxiv.org/abs/1509.04874
• demo: http://pan.baidu.com/s/1mgoWWsS
• KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php

 

 

 

 

 

SSD

SSD: Single Shot MultiBox Detector

• intro: ECCV 2016 Oral
• arxiv: http://arxiv.org/abs/1512.02325
• paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
• slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf
• github: https://github.com/weiliu89/caffe/tree/ssd
• video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
• github: https://github.com/zhreshold/mxnet-ssd
• github: https://github.com/zhreshold/mxnet-ssd.cpp
• github: https://github.com/rykov8/ssd_keras
• github: https://github.com/balancap/SSD-Tensorflow
• github: https://github.com/amdegroot/ssd.pytorch

 

 

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

• intro: 「0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it」.
• arxiv: http://arxiv.org/abs/1512.04143
• slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
• coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard

Adaptive Object Detection Using Adjacency and Zoom Prediction

• intro: CVPR 2016. AZ-Net
• arxiv: http://arxiv.org/abs/1512.07711
• github: https://github.com/luyongxi/az-net
• youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM

 

 

 

 

 

G-CNN

G-CNN: an Iterative Grid Based Object Detector

• arxiv: http://arxiv.org/abs/1512.07729

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

• intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
• project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
• arxiv: http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

• arxiv: http://arxiv.org/abs/1602.08405

 

 

HyperNet

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

• arxiv: http://arxiv.org/abs/1604.00600

 

 

MultiPathNet

A MultiPath Network for Object Detection

• intro: BMVC 2016. Facebook AI Research (FAIR)
• arxiv: http://arxiv.org/abs/1604.02135
• github: https://github.com/facebookresearch/multipathnet

 

 

CRAFT

CRAFT Objects from Images

• intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
• project page: http://byangderek.github.io/projects/craft.html
• arxiv: https://arxiv.org/abs/1604.03239
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
• github: https://github.com/byangderek/CRAFT

 

 

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

• intro: CVPR 2016 Oral. Online hard example mining (OHEM)
• arxiv: http://arxiv.org/abs/1604.03540
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
• github（Official）: https://github.com/abhi2610/ohem
• author page: http://abhinav-shrivastava.info/

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1604.05766

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

• intro: scale-dependent pooling (SDP), cascaded rejection clas-sifiers (CRC)
• paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf

 

 

 

 

 

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

• arxiv: http://arxiv.org/abs/1605.06409
• github: https://github.com/daijifeng001/R-FCN
• github: https://github.com/Orpine/py-R-FCN
• github(PyTorch): https://github.com/PureDiors/pytorch_RFCN
• github: https://github.com/bharatsingh430/py-R-FCN-multiGPU

Weakly supervised object detection using pseudo-strong labels

• arxiv: http://arxiv.org/abs/1607.04731

Recycle deep features for better object detection

• arxiv: http://arxiv.org/abs/1607.05066

 

 

 

MS-CNN

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

• intro: ECCV 2016
• intro: 640×480: 15 fps, 960×720: 8 fps
• arxiv: http://arxiv.org/abs/1607.07155
• github: https://github.com/zhaoweicai/mscnn
• poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf

Multi-stage Object Detection with Group Recursive Learning

• intro: VOC2007: 78.6%, VOC2012: 74.9%
• arxiv: http://arxiv.org/abs/1608.05159

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

• intro: WACV 2017. SubCNN
• arxiv: http://arxiv.org/abs/1604.04693
• github: https://github.com/yuxng/SubCNN

 

 

 

PVANET

PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

• intro: 「less channels with more layers」, concatenated ReLU, Inception, and HyperNet, batch normalization, residual connections
• arxiv: http://arxiv.org/abs/1608.08021
• github: https://github.com/sanghoon/pva-faster-rcnn
• leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

• intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation ofarXiv:1608.08021
• arxiv: https://arxiv.org/abs/1611.08588

 

 

 

GBD-Net

Gated Bi-directional CNN for Object Detection

• intro: The Chinese University of Hong Kong & Sensetime Group Limited
• paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
• mirror: https://pan.baidu.com/s/1dFohO7v

Crafting GBD-Net for Object Detection

• intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
• intro: gated bi-directional CNN (GBD-Net)
• arxiv: https://arxiv.org/abs/1610.02579
• github: https://github.com/craftGBD/craftGBD

 

 

 

 

 

 

StuffNet

StuffNet: Using ‘Stuff’ to Improve Object Detection

• arxiv: https://arxiv.org/abs/1610.05861

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

• arxiv: https://arxiv.org/abs/1610.09609

Hierarchical Object Detection with Deep Reinforcement Learning

• intro: Deep Reinforcement Learning Workshop (NIPS 2016)
• project page: https://imatge-upc.github.io/detection-2016-nipsws/
• arxiv: https://arxiv.org/abs/1611.03718
• slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
• github: https://github.com/imatge-upc/detection-2016-nipsws
• blog: http://jorditorres.org/nips/

Learning to detect and localize many objects from few examples

• arxiv: https://arxiv.org/abs/1611.05664

Speed/accuracy trade-offs for modern convolutional object detectors

• intro: Google Research
• arxiv: https://arxiv.org/abs/1611.10012

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

• arxiv: https://arxiv.org/abs/1612.01051
• github: https://github.com/BichenWuUCB/squeezeDet

 

 

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

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

Action-Driven Object Detection with Top-Down Visual Attentions

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

Beyond Skip Connections: Top-Down Modulation for Object Detection

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

 

 

 

YOLOv2

YOLO9000: Better, Faster, Stronger

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

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

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

 

 

 

 

DSSD

DSSD : Deconvolutional Single Shot Detector

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

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

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

Attentional Network for Visual Object Detection

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

 

CC-Net

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

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

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

https://arxiv.org/abs/1703.10295

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

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

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.03944

Spatial Memory for Context Reasoning in Object Detection

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

Improving Object Detection With One Line of Code

• intro: University of Maryland
• keywords: Soft-NMS
• arxiv: https://arxiv.org/abs/1704.04503
• github: https://github.com/bharatsingh430/soft-nms

Accurate Single Stage Detector Using Recurrent Rolling Convolution

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.05776
• github: https://github.com/xiaohaoChen/rrc_detection

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

https://arxiv.org/abs/1704.05775

 

 

 

Detection From Video

Learning Object Class Detectors from Weakly Annotated Video

• intro: CVPR 2012
• paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf

Analysing domain shift factors between videos and images for object detection

• arxiv: https://arxiv.org/abs/1501.01186

Video Object Recognition

• slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx

Deep Learning for Saliency Prediction in Natural Video

• intro: Submitted on 12 Jan 2016
• keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
• paper: https://hal.archives-ouvertes.fr/hal-01251614/document

 

 

T-CNN

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

• intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
• arxiv: http://arxiv.org/abs/1604.02532
• github: https://github.com/myfavouritekk/T-CNN

Object Detection from Video Tubelets with Convolutional Neural Networks

• intro: CVPR 2016 Spotlight paper
• arxiv: https://arxiv.org/abs/1604.04053
• paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
• gihtub: https://github.com/myfavouritekk/vdetlib

Object Detection in Videos with Tubelets and Multi-context Cues

• intro: SenseTime Group
• slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
• slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

• intro: BMVC 2016
• keywords: pseudo-labeler
• arxiv: http://arxiv.org/abs/1607.04648
• paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf

CNN Based Object Detection in Large Video Images

• intro: WangTao @ 愛奇藝
• keywords: object retrieval, object detection, scene classification
• slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf

Object Detection in Videos with Tubelet Proposal Networks

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

Flow-Guided Feature Aggregation for Video Object Detection

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

Video Object Detection using Faster R-CNN

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

 

 

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1609.06666

 

 

 

Object Detection on RGB-D

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

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

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

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

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

https://arxiv.org/abs/1703.03347

 

 

 

 

 

 

 

 

 

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

http://i.cs.hku.hk/~yzyu/vision.html

Large-scale optimization of hierarchical features for saliency prediction in natural images

• paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf

Predicting Eye Fixations using Convolutional Neural Networks

• paper: http://www.escience.cn/system/file?fileId=72648

Saliency Detection by Multi-Context Deep Learning

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

• arxiv: http://arxiv.org/abs/1510.05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

• paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

• arxiv: http://arxiv.org/abs/1603.00845
• github: https://github.com/imatge-upc/saliency-2016-cvpr

Recurrent Attentional Networks for Saliency Detection

• intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
• arxiv: http://arxiv.org/abs/1604.03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

• arxiv: http://arxiv.org/abs/1607.04730

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

• intro: CVPR 2016
• project page: http://cs-people.bu.edu/jmzhang/sod.html
• paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
• github: https://github.com/jimmie33/SOD
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

Salient Object Subitizing

• intro: CVPR 2015
• intro: predicting the existence and the number of salient objects in an image using holistic cues
• project page: http://cs-people.bu.edu/jmzhang/sos.html
• arxiv: http://arxiv.org/abs/1607.07525
• paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

• intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
• arxiv: http://arxiv.org/abs/1608.05177

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.05186

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

• arxiv: http://arxiv.org/abs/1608.08029

A Deep Multi-Level Network for Saliency Prediction

• arxiv: http://arxiv.org/abs/1609.01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1609.02077

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

• intro: DSCLRCN
• arxiv: https://arxiv.org/abs/1610.01708

Deeply supervised salient object detection with short connections

• arxiv: https://arxiv.org/abs/1611.04849

Weakly Supervised Top-down Salient Object Detection

• intro: Nanyang Technological University
• arxiv: https://arxiv.org/abs/1611.05345

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

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

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

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

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

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

Saliency Detection by Forward and Backward Cues in Deep-CNNs

https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

https://arxiv.org/abs/1704.07242

 

 

 

 

Saliency Detection in Video

Deep Learning For Video Saliency Detection

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

 

 

 

 

 

 

Visual Relationship Detection

 

Visual Relationship Detection with Language Priors

• intro: ECCV 2016 oral
• paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
• github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

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

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

Visual Translation Embedding Network for Visual Relation Detection

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

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

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

Detecting Visual Relationships with Deep Relational Networks

• intro: CVPR 2017 oral. The Chinese University of Hong Kong
• arxiv: https://arxiv.org/abs/1704.03114

 

 

Specific Object Deteciton

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

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

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

• project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html

Compact Convolutional Neural Network Cascade for Face Detection

• arxiv: http://arxiv.org/abs/1508.01292
• github: https://github.com/Bkmz21/FD-Evaluation

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1606.00850
• github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D

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

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

Finding Tiny Faces

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

Towards a Deep Learning Framework for Unconstrained Face Detection

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

Supervised Transformer Network for Efficient Face Detection

• arxiv: http://arxiv.org/abs/1607.05477

UnitBox

UnitBox: An Advanced Object Detection Network

• intro: ACM MM 2016
• arxiv: http://arxiv.org/abs/1608.01471

Bootstrapping Face Detection with Hard Negative Examples

• author: 萬韶華 @ 小米.
• intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
• arxiv: http://arxiv.org/abs/1608.02236

Grid Loss: Detecting Occluded Faces

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1609.00129
• paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
• poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

• intro: ICPR 2016
• arxiv: http://arxiv.org/abs/1609.03536

 

 

 

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

• project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
• arxiv: https://arxiv.org/abs/1604.02878
• github(Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
• github: https://github.com/pangyupo/mxnet_mtcnn_face_detection
• github: https://github.com/DaFuCoding/MTCNN_Caffe
• github(MXNet): https://github.com/Seanlinx/mtcnn
• github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
• github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
• github: https://github.com/CongWeilin/mtcnn-caffe

Face Detection using Deep Learning: An Improved Faster RCNN Approach

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

Faceness-Net: Face Detection through Deep Facial Part Responses

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

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained 「Hard Faces」

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

End-To-End Face Detection and Recognition

https://arxiv.org/abs/1703.10818

 

 

 

 

Facial Point / Landmark Detection

Deep Convolutional Network Cascade for Facial Point Detection

• homepage: http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
• github: https://github.com/luoyetx/deep-landmark

Facial Landmark Detection by Deep Multi-task Learning

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

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1608.05477

Detecting facial landmarks in the video based on a hybrid framework

• arxiv: http://arxiv.org/abs/1609.06441

Deep Constrained Local Models for Facial Landmark Detection

• arxiv: https://arxiv.org/abs/1611.08657

Effective face landmark localization via single deep network

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

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

https://arxiv.org/abs/1704.01880

 

 

 

People Detection

End-to-end people detection in crowded scenes

• arxiv: http://arxiv.org/abs/1506.04878
• github: https://github.com/Russell91/reinspect
• ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb

Detecting People in Artwork with CNNs

• intro: ECCV 2016 Workshops
• arxiv: https://arxiv.org/abs/1610.08871

Deep Multi-camera People Detection

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

 

 

 

 

 

 

 

 

Person Head Detection

Context-aware CNNs for person head detection

• arxiv: http://arxiv.org/abs/1511.07917
• github: https://github.com/aosokin/cnn_head_detection

 

 

Pedestrian Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

• intro: CVPR 2015
• project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
• paper: http://arxiv.org/abs/1412.0069

Deep Learning Strong Parts for Pedestrian Detection

• intro: ICCV 2015. CUHK. DeepParts
• intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

Deep convolutional neural networks for pedestrian detection

• arxiv: http://arxiv.org/abs/1510.03608
• github: https://github.com/DenisTome/DeepPed

Scale-aware Fast R-CNN for Pedestrian Detection

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

New algorithm improves speed and accuracy of pedestrian detection

• blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php

Pushing the Limits of Deep CNNs for Pedestrian Detection

• intro: 「set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%」
• arxiv: http://arxiv.org/abs/1603.04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

• arxiv: http://arxiv.org/abs/1607.04436

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

• arxiv: http://arxiv.org/abs/1607.04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.07032
• github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Reduced Memory Region Based Deep Convolutional Neural Network Detection

• intro: IEEE 2016 ICCE-Berlin
• arxiv: http://arxiv.org/abs/1609.02500

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

• arxiv: https://arxiv.org/abs/1610.03466

Multispectral Deep Neural Networks for Pedestrian Detection

• intro: BMVC 2016 oral
• arxiv: https://arxiv.org/abs/1611.02644

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

• intro: CVPR 2017
• project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
• arxiv: https://arxiv.org/abs/1703.06283
• github(Tensorflow): https://github.com/huangshiyu13/RPNplus

 

 

 

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.04564

Evolving Boxes for fast Vehicle Detection

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

 

 

 

 

 

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

• project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
• paper: http://120.52.73.11/www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
• code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip

 

 

Boundary / Edge / Contour Detection

Holistically-Nested Edge Detection

• intro: ICCV 2015, Marr Prize
• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xie_Holistically-Nested_Edge_Detection_ICCV_2015_paper.pdf
• arxiv: http://arxiv.org/abs/1504.06375
• github: https://github.com/s9xie/hed

Unsupervised Learning of Edges

• intro: CVPR 2016. Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.04166
• zn-blog: http://www.leiphone.com/news/201607/b1trsg9j6GSMnjOP.html

Pushing the Boundaries of Boundary Detection using Deep Learning

• arxiv: http://arxiv.org/abs/1511.07386

Convolutional Oriented Boundaries

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.02755

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

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

Richer Convolutional Features for Edge Detection

• intro: richer convolutional features (RCF)
• arxiv: https://arxiv.org/abs/1612.02103

 

 

Skeleton Detection

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

• arxiv: http://arxiv.org/abs/1603.09446
• github: https://github.com/zeakey/DeepSkeleton

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

• arxiv: http://arxiv.org/abs/1609.03659

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

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

 

 

Fruit Detection

Deep Fruit Detection in Orchards

• arxiv: https://arxiv.org/abs/1610.03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

• intro: The Journal of Field Robotics in May 2016
• project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
• arxiv: https://arxiv.org/abs/1610.08120

 

 

Part Detection

Objects as context for part detection

https://arxiv.org/abs/1703.09529

 

 

Others

Deep Deformation Network for Object Landmark Localization

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

Fashion Landmark Detection in the Wild

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

Deep Learning for Fast and Accurate Fashion Item Detection

• intro: Kuznech Inc.
• intro: MultiBox and Fast R-CNN
• paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as 「OSM-Crosswalk-Detection」)

• github: https://github.com/geometalab/OSMDeepOD

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

• intro: IEEE SITIS 2016
• arxiv: https://arxiv.org/abs/1611.04357

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

• arxiv: https://arxiv.org/abs/1611.05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

• intro: CMU & Magic Leap
• arxiv: https://arxiv.org/abs/1611.10010

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

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

Deep Learning Logo Detection with Data Expansion by Synthesising Context

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

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

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

Automatic Handgun Detection Alarm in Videos Using Deep Learning

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

 

 

 

 

 

 

Object Proposal

 

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

• arxiv: http://arxiv.org/abs/1510.04445
• github: https://github.com/aghodrati/deepproposal

Scale-aware Pixel-wise Object Proposal Networks

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1601.04798

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

• intro: BMVC 2016. AttractioNet
• arxiv: https://arxiv.org/abs/1606.04446
• github: https://github.com/gidariss/AttractioNet

Learning to Segment Object Proposals via Recursive Neural Networks

• arxiv: https://arxiv.org/abs/1612.01057

Learning Detection with Diverse Proposals

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

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

• keywords: product detection
• arxiv: https://arxiv.org/abs/1704.06752

Improving Small Object Proposals for Company Logo Detection

• intro: ICMR 2017
• arxiv: https://arxiv.org/abs/1704.08881

 

 

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

• intro: PhD Thesis
• homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
• phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
• github(「SDS using hypercolumns」): https://github.com/bharath272/sds

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

• arxiv: http://arxiv.org/abs/1503.00949

Weakly Supervised Object Localization Using Size Estimates

• arxiv: http://arxiv.org/abs/1608.04314

Active Object Localization with Deep Reinforcement Learning

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

Localizing objects using referring expressions

• intro: ECCV 2016
• keywords: LSTM, multiple instance learning (MIL)
• paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
• github: https://github.com/varun-nagaraja/referring-expressions

LocNet: Improving Localization Accuracy for Object Detection

• arxiv: http://arxiv.org/abs/1511.07763
• github: https://github.com/gidariss/LocNet

Learning Deep Features for Discriminative Localization

• homepage: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150
• github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
• github: https://github.com/metalbubble/CAM
• github: https://github.com/tdeboissiere/VGG16CAM-keras

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

• intro: ECCV 2016
• project page: http://www.di.ens.fr/willow/research/contextlocnet/
• arxiv: http://arxiv.org/abs/1609.04331
• github: https://github.com/vadimkantorov/contextlocnet

 

 

 

 

Tutorials / Talks

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

• slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Towards Good Practices for Recognition & Detection

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

 

 

 

 

 

 

 

Projects

TensorBox: a simple framework for training neural networks to detect objects in images

• intro: 「The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of theReInspect algorithm」
• github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

• github: https://github.com/fmassa/object-detection.torch

Using DIGITS to train an Object Detection network

• github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

FCN-MultiBox Detector

• intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
• github: https://github.com/teaonly/FMD.torch

KittiBox: A car detection model implemented in Tensorflow.

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

 

 

 

Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

 

 

 

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

• keywords: Faster R-CNN
• blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
• demo: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
• review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

• blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

Analyzing The Papers Behind Facebook’s Computer Vision Approach

• keywords: DeepMask, SharpMask, MultiPathNet
• blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

Easily Create High Quality Object Detectors with Deep Learning

• intro: dlib v19.2
• blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

• blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
• github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

Object Detection in Satellite Imagery, a Low Overhead Approach

• part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
• part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

• part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
• part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

• blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
• ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
• github: https://github.com/bigsnarfdude/Faster-RCNN_TF

Small U-Net for vehicle detection

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

Region of interest pooling explained

• blog: https://deepsense.io/region-of-interest-pooling-explained/
• github: https://github.com/deepsense-io/roi-pooling

 

 

 

 

 

 

 

 

Deep Learning（深度學習）：

ufldl的2個教程(這個沒得說，入門絕對的好教程，Ng的，邏輯清晰有練習)：一

ufldl的2個教程(這個沒得說，入門絕對的好教程，Ng的，邏輯清晰有練習)：二

Bengio團隊的deep learning教程，用的theano庫，主要是rbm系列，搞python的能夠參考，很不錯。

deeplearning.net主頁，裏面包含的信息量很是多，有software, reading list, research lab, dataset, demo等，強烈推薦，本身去發現好資料。

Deep learning的toolbox，matlab實現的，對應源碼來學習一些常見的DL模型頗有幫助，這個庫我主要是用來學習算法實現過程的。

2013年龍星計劃深度學習教程，鄧力大牛主講，雖然老師準備得不充分，不過仍是頗有收穫的。

Hinton大牛在coursera上開的神經網絡課程，DL部分有很多，很是贊，沒有廢話，課件每句話都包含了不少信息，有必定DL基礎後去聽收穫更大。

Larochelle關於DL的課件，邏輯清晰，覆蓋面廣,包含了rbm系列，autoencoder系列，sparse coding系列，還有crf，cnn，rnn等。雖然網頁是法文，可是課件是英文。

CMU大學2013年的deep learning課程，有很多reading paper能夠參考。

達慕思大學Lorenzo Torresani的2013Deep learning課程reading list.

Deep Learning Methods for Vision(餘凱等在cvpr2012上組織一個workshop，關於DL在視覺上的應用)。

斯坦福Ng團隊成員連接主頁，能夠進入團隊成員的主頁，比較熟悉的有Richard Socher, Honglak Lee, Quoc Le等。

多倫多ML團隊成員連接主頁，能夠進入團隊成員主頁，包括DL鼻祖hinton，還有Ruslan Salakhutdinov , Alex Krizhevsky等。

蒙特利爾大學機器學習團隊成員連接主頁，包括大牛Bengio，還有Ian Goodfellow 等。

紐約大學的機器學習團隊成員連接主頁，包括大牛Lecun，還有Rob Fergus等。

Charlie Tang我的主頁，結合DL+SVM.

豆瓣上的腦與deep learning讀書會，有講義和部分視頻，主要介紹了一些於deep learning相關的生物神經網絡。

Large Scale ML的課程，由Lecun和Langford講的，能不推薦麼。

Yann Lecun的2014年Deep Learning課程主頁。 視頻連接。 

吳立德老師《深度學習課程》

一些常見的DL code列表，csdn博主zouxy09的博文，Deep Learning源代碼收集-持續更新…

Deep Learning for NLP (without Magic)，由DL界5大高手之一的Richard Socher小組搞的，他主要是NLP的。

2012 Graduate Summer School: Deep Learning, Feature Learning，高手雲集，深度學習盛宴，幾乎全部的DL大牛都有參加。

matlab下的maxPooling速度優化，調用C++實現的。

2014年ACL機器學習領域主席Kevin Duh的深度學習入門講座視頻。

R-CNN code: Regions with Convolutional Neural Network Features.

 

 

 

Machine Learning（機器學習）：

介紹圖模型的一個ppt，很是的贊，ppt做者總結得很給力，裏面還包括了HMM，MEM, CRF等其它圖模型。反正看完挺有收穫的。

機器學習一個視頻教程，youtube上的，翻吧，內容很全面，偏機率統計模型，每一小集只有幾分鐘。 

龍星計劃2012機器學習，由余凱和張潼主講。

demonstrate 的 blog :關於PGM(機率圖模型)系列，主要按照Daphne Koller的經典PGM教程介紹的，你們依次google之。

FreeMind的博客，主要關於機器學習的。

Tom Mitchell大牛的機器學習課程，他的machine learning教科書很是出名。

CS109,Data Science,用python介紹機器學習算法的課程。

CCF主辦的一些視頻講座。

 

 

 

國外技術團隊博客：

Netflix技術博客,不少乾貨。

 

 

 

Computer Vision（計算機視覺）：

MIT2013年秋季課程：Advances in Computer Vision，有練習題，有些有code.

IPAM一個計算機視覺的短時間課程，有很多牛人蔘加。

 

 

 

 

OpenCV相關：

http://opencv.org/

2012年7月4日隨着opencv2.4.2版本的發佈，opencv更改了其最新的官方網站地址。

http://www.opencvchina.com/

好像12年纔有這個論壇的，比較新。裏面有針對《learning opencv》這本書的視頻講解，不過視頻教學還沒出完，正在更新中。對剛入門學習opencv的人來講很不錯。

http://www.opencv.org.cn/forum/

opencv中文論壇，對於初次接觸opencv的學者來講比較不錯，入門資料多，opencv的各類英文文檔也翻譯成中文了。不足是感受這個論壇上發帖提問不多人回答，也就是說討論不夠激烈。

http://opencv.jp/

opencv的日文網站，裏面有很多例子代碼，看不懂日文能夠用網站自帶的翻譯，能看個大概。

http://code.opencv.org/projects/opencv

opencv版本bug修補，版本更新，以及各類相關大型活動安排,還包含了opencv最近幾個月內的活動路線，即將來將增長的功能等，能夠掌握各類關於opencv進展狀況的最新進展。

http://tech.groups.yahoo.com/group/OpenCV/

opencv雅虎郵件列表，聽說是最好的opencv論壇，信息更新最新的地方。不過我的認爲要查找相關主題的內容，在郵件列表中很是不方便。

http://www.cmlab.csie.ntu.edu.tw/~jsyeh/wiki/doku.php

臺灣大學暑假集訓網站，內有連接到與opencv集訓相關的網頁。感受這種教育形式還蠻不錯的。

http://sourceforge.net/projects/opencvlibrary/

opencv版本發佈地方。

http://code.opencv.org/projects/opencv/wiki/ChangeLog#241    http://opencv.willowgarage.com/wiki/OpenCV%20Change%20Logs

opencv版本內容更改日誌網頁,前面那個網頁更新最快。

http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/tutorials.html

opencv中文教程網頁，分幾個模塊講解，有代碼有過程。內容是網友翻譯opencv自帶的doc文件裏的。

https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

網友總結的經常使用帶有cvpr領域常見算法code連接的網址，感受很是的不錯。

http://fossies.org/dox/OpenCV-2.4.2/

該網站能夠查看opencv中一些函數的變量接口，還會列出函數之間的結構圖。

http://opencv.itseez.com/

opencv的函數、類等查找網頁，有導航，查起來感受不錯。

 

 

 

優化：

submodual優化網頁。

Geoff Gordon的優化課程，youtube上有對應視頻。

 

 

 

數學：

http://www.youku.com/playlist_show/id_19465801.html

《計算機中的數學》系列視頻，8位老師10講內容，生動介紹微積分和線性代數基本概念在計算機學科中的各類有趣應用！

 

 

 

Linux學習資料：

http://itercast.com/library/1

linux入門的基礎視頻教程，對於新手可選擇看第一部分，視頻來源於LinuxCast.net網站，還不錯。

 

 

 

OpenNI+Kinect相關：

http://1.yuhuazou.sinaapp.com/

網友晨宇思遠的博客，主攻cvpr，ai等。

http://blog.csdn.net/chenli2010/article/details/6887646

kinect和openni學習資料彙總。

http://blog.csdn.net/moc062066/article/category/871261

OpenCV 計算機視覺 kinect的博客:

http://kheresy.wordpress.com/index_of_openni_and_kinect/comment-page-5/

網友Heresy的博客，裏面有很多kinect的文章，寫的比較詳細。

http://www.cnkinect.com/

體感遊戲中文網，有很多新的kinect資訊。

http://www.kinectutorial.com/

Kinect體感開發網。

http://code.google.com/p/openni-hand-tracker

openni_hand_tracking google code項目。

http://blog.candescent.ch/

網友的kinect博客，裏面有不少手勢識別方面的文章介紹，還有源碼，不過貌似是基於c#的。

https://sites.google.com/site/colordepthfusion/

一些關於深度信息和顏色信息融合（fusion）的文章。

http://projects.ict.usc.edu/mxr/faast/

kinect新的庫，能夠結合OpenNI使用。

https://sites.google.com/a/chalearn.org/gesturechallenge/

kinect手勢識別網站。

http://www.ros.org/wiki/mit-ros-pkg

mit的kinect項目，有code。主要是與手勢識別相關。

http://www.thoughtden.co.uk/blog/2012/08/kinecting-people-our-top-6-kinect-projects/

kinect 2012年度最具創新的6個項目，有視頻，確實夠創新的！

http://www.cnblogs.com/yangyangcv/archive/2011/01/07/1930349.html

kinect多點觸控的一篇博文。

http://sourceforge.net/projects/kinect-mex/

http://www.mathworks.com/matlabcentral/fileexchange/30242-kinect-matlab

有關matlab for kinect的一些接口。

http://news.9ria.com/2012/1212/25609.html

AIR和Kinect的結合，有一些手指跟蹤的code。

http://eeeweba.ntu.edu.sg/computervision/people/home/renzhou/index.htm

研究kinect手勢識別的，任洲。剛畢業不久。

 

 

 

其餘網友cvpr領域的連接總結：

http://www.cnblogs.com/kshenf/

網友整理經常使用牛人連接總結，很是多。不過我的沒有沒有每一個網站都去試過。因此本文也是我本身總結本身曾經用過的或體會過的。

 

 

 

OpenGL有關:

http://nehe.gamedev.net/

NeHe的OpenGL教程英文版。

http://www.owlei.com/DancingWind/

NeHe的OpenGL教程對應的中文版，由網友周瑋翻譯的。

http://www.qiliang.net/old/nehe_qt/

NeHe的OpengGL對應的Qt版中文教程。

http://blog.csdn.net/qp120291570

網友"左腦設計，右腦編程"的Qt_OpenGL博客,寫得還不錯。

http://guiliblearning.blogspot.com/

這個博客對opengl的機制有所剖析，貌似要FQ才能進去。

 

 

 

 

cvpr綜合網站論壇博客等：

http://www.cvchina.net/

中國計算機視覺論壇

http://www.cvchina.info/

這個博客很不錯，每次看完都能讓人興奮，由於有不少關於cv領域的科技新聞，還時不時有視頻顯示。另外這個博客裏面的資源也整理得至關不錯。中文的。

http://www.bfcat.com/

一位網友的我的計算機視覺博客，有不少關於計算機視覺前沿的東西介紹，與上面的博客同樣，看了也能讓人興奮。

http://blog.csdn.net/v_JULY_v/

牛人博客，主攻數據結構，機器學習數據挖掘算法等。

http://blog.youtueye.com/

該網友上面有一些計算機視覺方向的博客,博客中附有一些實驗的測試代碼.

http://blog.sciencenet.cn/u/jingyanwang

多看pami才扯談的博客，其中有很多pami文章的中文介紹。

http://chentingpc.me/

作網絡和天然語言處理的，有很多機器學習方面的介紹。

 

 

 

 

ML經常使用博客資料等：

http://freemind.pluskid.org/

由 pluskid 所維護的 blog，主要記錄一些機器學習、程序設計以及各類技術和非技術的相關內容，寫得很不錯。

http://datasciencemasters.org/

裏面包含學ML/DM所須要的一些知識連接，且有些給出了視頻教程，網頁資料，電子書，開源code等，推薦！

http://cs.nju.edu.cn/zhouzh/index.htm

周志華主頁，不用介紹了，機器學習大牛，更難得的是他的不少文章都有源碼公佈。

http://www.eecs.berkeley.edu/~jpaisley/Papers.htm

John Paisley的我的主頁，主要研究機器學習領域，有些文章有代碼提供。

http://foreveralbum.yo2.cn/

裏面有一些常見機器學習算法的詳細推導過程。

http://blog.csdn.net/abcjennifer

浙江大學CS碩士在讀，關注計算機視覺，機器學習，算法研究，博弈， 人工智能， 移動互聯網等學科和產業。該博客中有不少機器學習算法方面的介紹。

http://www.wytk2008.net/

無垠天空的機器學習博客。

http://www.chalearn.org/index.html

機器學習挑戰賽。

http://licstar.net/

licstar的技術博客，偏天然語言處理方向。

 

 

 

 

國內科研團隊和牛人網頁：

http://vision.ia.ac.cn/zh/index_cn.html

中科院自動化所機器視覺課題小組，有相關數據庫、論文、課件等下載。

http://www.cbsr.ia.ac.cn/users/szli/

李子青教授我的主頁，中科院自動化所cvpr領域牛叉人！

http://www4.comp.polyu.edu.hk/~cslzhang/

香港理工大學教授lei zhang我的主頁，也是cvpr領域一大牛人啊，cvpr，iccv各類發表。更重要的是他因此牛叉論文的code所有公開，很是可貴！

http://liama.ia.ac.cn/wiki/start

中法信息、自動化與應用聯合實驗室，裏面不少內容不只限而cvpr，還有ai領域一些其餘的研究。

http://www.cogsci.xmu.edu.cn/cvl/english/

廈門大學特聘教授，cv領域一位牛人。研究方向主要爲目標檢測，目標跟蹤，運動估計，三維重建，魯棒統計學，光流計算等。

http://idm.pku.edu.cn/index.aspx

北京大學數字視頻編碼技術國家實驗室。 

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

libsvm項目網址，臺灣大學的，很火！

http://www.jdl.ac.cn/user/sgshan/index.htm

山世光，人臉識別研究比較牛。在中國科學院智能信息處理重點實驗室

 

 

 

 

國外科研團隊和牛人網頁：

https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

常見計算機視覺資源整理索引，國外學者整理，全是出名的算法，而且帶有代碼的，這個很是有幫助，其連接都是相關領域很火的代碼。

http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/txtv-groups.html

國外學者整理的各高校研究所團隊網站

http://research.microsoft.com/en-us/groups/vision/

微軟視覺研究小組，不解釋，你們懂的，牛！

http://lear.inrialpes.fr/index.php

法國國家信息與自動化研究所，有對應牛人的連接，論文項目網頁連接，且一些code對應連接等。

http://www.cs.ubc.ca/~pcarbo/objrecls/

Learning to recognize objects with little supervision該篇論文的項目網頁，有對應的code下載，另附有詳細說明。

http://www.eecs.berkeley.edu/~lbourdev/poselets/

poselets相關研究界面，關於poselets的第一手資料。

http://www.cse.oulu.fi/CMV/Research

芬蘭奧盧大學計算機科學與工程學院網頁，裏面有不少cv領域相關的研究，好比說人臉，臉部表情，人體行爲識別，跟蹤，人機交互等cv基本都涉及有。

http://www.cs.cmu.edu/~cil/vision.html

卡耐基梅隆大學計算機視覺主頁，內容很是多。惋惜的是該網站內容只更新到了2004年。

http://vision.stanford.edu/index.html

斯坦福大學計算機視覺主頁，裏面有很是很是多的牛人，好比說你們熟悉的lifeifei.

http://www.wavelet.org/index.php

關於wavelet研究的網頁。

http://civs.ucla.edu/

加州大學洛杉磯分校統計學院，關於統計學習方面各類資料，且有相應的網上公開課。

http://www.cs.cmu.edu/~efros/

卡耐基梅隆大學Alexei(Alyosha)Efros教授我的網站，計算機圖形學高手。

http://web.mit.edu/torralba/www//

mit牛人Associate教授我的網址，主要研究計算機視覺人體視覺感知，目標識別和場景理解等。

http://people.csail.mit.edu/billf/

mit牛人William T. Freeman教授，主要研究計算機視覺和圖像學

http://www.research.ibm.com/peoplevision/

IBM人體視覺研究中心，裏面除了有其研究小組的最新成果外，還有不少測試數據(特別是視頻）供下載。

http://www.vlfeat.org/

vlfeat主頁，vlfeat也是一個開源組織，主要定位在一些最流行的視覺算法開源上，Ｃ編寫，其不少算法效果比opencv要好,不過數量不全，可是很是有用。

http://www.robots.ox.ac.uk/~az/

Andrew Zisserman的我的主頁，這人你們應該熟悉，《計算機視覺中的多視幾何》這本神書的做者之一。

http://www.cs.utexas.edu/~grauman/

KristenGrauman教授的我的主頁，是個大美女，且是2011年「馬爾獎」得到者，」馬爾獎「你們都懂的，計算機視覺領域的最高獎項，目前無一個國內學者得到過。她的主要研究方法是視覺識別。

http://groups.csail.mit.edu/vision/welcome/

mit視覺實驗室主頁。

http://code.google.com/p/sixthsense/

曾經在網絡上很是出名一個視頻，一個做者研究的第六感裝置，如今這個就是其開源的主頁。

http://vision.ucsd.edu/~pdollar/research.html#BehaviorRecognitionAnimalBehavior

Piotr Dollar的我的主要，主要研究方向是人體行爲識別。

http://www.mmp.rwth-aachen.de/

移動多媒體處理，將移動設備，計算機圖像學，視覺，圖像處理等結合的領域。

http://www.di.ens.fr/~laptev/index.html

Ivan Laptev牛人主頁，主要研究人體行爲識別。有不少數據庫能夠下載。

http://blogs.oregonstate.edu/hess/

Rob Hess的我的主要，裏面有源碼下載，好比說粒子濾波，他寫的粒子濾波在網上很火。

http://morethantechnical.googlecode.com/svn/trunk/

cvpr領域一些小型的開源代碼。

http://iica.de/pd/index.py

作行人檢測的一個團隊，內部有一些行人檢測的代碼下載。

http://www.cs.utexas.edu/~grauman/research/pubs.html

UT-Austin計算機視覺小組，包含的視覺研究方向比較廣，且有的文章有源碼，你只須要填一個郵箱地址，系統會自動發跟源碼相關的信息過來。

http://www.robots.ox.ac.uk/~vgg/index.html

visual geometry group

 

 

 

 

圖像:

http://blog.sina.com.cn/s/blog_4cccd8d301012pw5.html

交互式圖像分割代碼。

http://vision.csd.uwo.ca/code/

graphcut優化代碼。

 

 

 

 

語音：

http://danielpovey.com/kaldi-lectures.html

語音處理中的kaldi學習。

 

 

 

 

算法分析與設計（計算機領域的基礎算法）：

http://www.51nod.com/focus.html

該網站主要是討論一些算法題。裏面的李陶冶是個大牛，回答了不少算法題。

 

 

 

一些綜合topic列表：

http://www.cs.cornell.edu/courses/CS7670/2011fa/

計算機視覺中的些topic（Special Topics in Computer Vision），截止到2011年爲止，其引用的文章都是很是頂級的topic。

 

 

 

 

書籍相關網頁：

http://www.imageprocessingplace.com/index.htm

岡薩雷斯的《數字圖像處理》一書網站，包含課程材料，matlab圖像處理工具包，課件ppt等相關素材。

Consumer Depth Cameras for Computer Vision

很優秀的一本書，不過很貴，買不起啊！作深度信息的使用這本書還不錯，google圖中能夠預覽一部分。

Making.Things.See

針對Kinect寫的，主要關注深度信息，較爲基礎。書籍中有很多例子，貌似是java寫的。

 

 

 

國內一些AI相關的研討會：

http://www.iipl.fudan.edu.cn/MLA13/index.htm

中國機器學習及應用研討會(這個是2013年的)

 

 

 

期刊會議論文下載：

http://cvpapers.com/

幾個頂級會議論文公開下載界面，好比說ICCV,CVPR,ECCV,ACCV,ICPR,SIGGRAPH等。

http://www.cvpr2012.org/

cvpr2012的官方地址，裏面有各類資料和信息，其餘年份的地址相似推理更改便可。

http://www.sciencedirect.com/science/journal/02628856

ICV期刊下載

http://www.computer.org/portal/web/tpami

TPAMI期刊，AI領域中能夠算得上是最頂級的期刊了，裏面有很多cvpr方面的內容。

http://www.springerlink.com/content/100272/

IJCV的網址。

http://books.nips.cc/

NIPS官網，有論文下載列表。

http://graphlab.org/lsrs2013/program/

LSRS （會議）地址，大規模推薦系統，其它年份依次類推。

 

 

 

會議期刊相關信息：

http://conferences.visionbib.com/Iris-Conferences.html

該網頁列出了圖像處理，計算機視覺領域相關幾乎全部比較出名的會議時間表。

http://conferences.visionbib.com/Browse-conf.php

上面網頁的一個子網頁，列出了最近的CV領域提交paper的deadline。

 

 

 

cvpr相關數據庫下載：

http://research.microsoft.com/en-us/um/people/jckrumm/WallFlower/TestImages.htm

微軟研究院牛人Wallflower Paper的論文中用到的目標檢測等測試圖片

http://archive.ics.uci.edu/ml/

UCI數據庫列表下載，最經常使用的機器學習數據庫列表。

http://www.cs.rochester.edu/~rmessing/uradl/

人體行爲識別經過關鍵點的跟蹤視頻數據庫，Rochester university的

http://www.research.ibm.com/peoplevision/performanceevaluation.html

IBM人體視覺研究中心，有視頻監控等很是多的測試視頻。

http://www.cvpapers.com/datasets.html

該網站上列出了常見的cvpr研究的數據庫。

http://www.cs.washington.edu/rgbd-dataset/index.html

RGB-D Object Dataset.作目標識別的。

 

 

 

AI相關娛樂網頁：

http://en.akinator.com/

該網站很好玩，能夠測試你內心想出的一我的名(固然前提是這我的必須有必定的知名度)，而後該網站會提出一系列的問題，你能夠選擇yes or no,or I don’t know等等，最後系統會顯示你心中所想的那我的。

http://www.doggelganger.co.nz/

人與狗的匹配遊戲，攝像頭採集人臉，呵呵…

 

 

 

 

Android相關：

https://code.google.com/p/android-ui-utils/

該網站上有一些android圖標,菜單等跟界面有關的設計工具,能夠用來作一些簡單的UI設計.

 

 

 

 

工具和code下載：

http://lear.inrialpes.fr/people/dorko/downloads.html

6種常見的圖像特徵點檢測子，linux下環境運行。不過只提供了二進制文件，不提供源碼。

http://www.cs.ubc.ca/~pcarbo/objrecls/index.html#code

ssmcmc的matlab代碼,是Learning to recognize objects with little supervision這一系列文章用的源碼，屬於目標識別方面的研究。

http://www.robots.ox.ac.uk/~timork/

仿射無關尺度特徵點檢測算子源碼，還有些其它算子的源碼或二進制文件。

http://www.vision.ee.ethz.ch/~bleibe/code/ism.html

隱式形狀模型(ISM)項目主頁，做者Bastian Leibe提供了linux下運行的二進制文件。

http://www.di.ens.fr/~laptev/download.html#stip

Ivan Laptev牛人主頁中的STIP特徵點檢測code，可是也只是有二進制文件，無源碼。該特徵點在行爲識別中該特徵點很是有名。

http://ai.stanford.edu/~quocle/

斯坦福大學Quoc V.Le主頁，上有它2011年行爲識別文章的代碼。

 

 

 

 

開源軟件：

http://mloss.org/software/

一些ML開源軟件在這裏基本均可以搜到，有上百個。

https://github.com/myui/hivemall

Scalable machine learning library for Hive/Hadoop.

http://scikit-learn.org/stable/

 

基於python的機器學習開源軟件，文檔寫得不錯。

 

 

 

 

挑戰賽：

http://www.chioka.in/kaggle-competition-solutions/

kaggle一些挑戰賽的code. 

 

 

 

 

公開課：

網易公開課，國內作得很不錯的公開課，翻譯了一些國外出名的公開課教程，與國外公開課平臺coursera有合做。

coursera在線教育網上公開課，很新，有個郵箱註冊便可學習，有很多課程，且有對應的練習，特別是編程練習，超讚。

斯坦福網上公開課連接，有統計學習，凸優化等課程。

udacity公開課程下載連接，其實速度還能夠。裏面有很多好教程。

機器學習公開課的鏈接，有很多課。

 

 

 

 

 　　在最近的學習中，看到一些有用的資源就記下來了，如今總結一下，歡迎補充！ 
機器視覺開源代碼合集 
計算機視覺算法與代碼集錦 
計算機視覺的一些測試數據集和源碼站點 
SIFT官網 
SURF PCA-SIFT and SIFT 開源代碼 總結 
經常使用圖像數據集：標註、檢索 
KTH-TIPS2 image dataset 
視頻中行爲識別公開數據庫彙總 
MSR Action Recognition Datasets and Codes 
Sparse coding simulation software 
稀疏表示 
Deep Learning源代碼收集-持續更新 
Training a deep autoencoder or a classifier on MNIST digits 
Charlie Tang 
本文實現了09年CVPR的文章 
Kaggle 機器學習競賽冠軍及優勝者的源代碼彙總 
Feature_detection 
機器學習視頻公開課 
機器學習的最佳入門學習資源 
http://blog.jobbole.com/82630/ 
國外程序員整理的機器學習資源大全 
一些下載資源的連接 
Some Useful Links 
A Library for Large Linear Classification

 

 

 

 

 

 

 

 

 

 

本博文轉自

http://blog.csdn.net/huixingshao/article/details/71406084

https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#t-cnn

本人經常使用資源整理(ing...)

做者：好記性不如爛筆頭！
出處：http://www.cnblogs.com/zlslch/

本文版權歸做者和博客園共有，歡迎轉載，但未經做者贊成必須保留此段聲明，且在文章頁面明顯位置給出原文連接，不然保留追究法律責任的權利。

 

分類:  機器學習/深度學習