本文转载自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#
(接Object Detection博客(上))
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://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
Detecting Small Signs from Large Images
intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
arxiv: https://arxiv.org/abs/1706.08574
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: CVPR 2017
keywords: richer convolutional features (RCF)
arxiv: https://arxiv.org/abs/1612.02103
github: https://github.com/yun-liu/rcf
Contour Detection from Deep Patch-level Boundary Prediction
https://arxiv.org/abs/1705.03159
CASENet: Deep Category-Aware Semantic Edge Detection
intro: CVPR 2017
arxiv: https://arxiv.org/abs/1705.09759
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
Using Deep Networks for Drone Detection
intro: AVSS 2017
arxiv: https://arxiv.org/abs/1706.05726
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
intro: CVPR 2016 oral
arxiv: http://arxiv.org/abs/1511.07763
github: https://github.com/gidariss/LocNet
Learning Deep Features for Discriminative Localization
[图片上传中。。。（1）]
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
Ensemble of Part Detectors for Simultaneous Classification and Localization
https://arxiv.org/abs/1705.10034
Tutorials / Talks
****Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection**
slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf
Towards Good Practices for Recognition & Detection
intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf
Projects
****TensorBox: a simple framework for training neural networks to detect objects in images**
intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github: https://github.com/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
github: https://github.com/fmassa/object-detection.torch
Using DIGITS to train an Object Detection network
github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md
FCN-MultiBox Detector
intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
github: https://github.com/teaonly/FMD.torch
KittiBox: A car detection model implemented in Tensorflow.
keywords: MultiNet
intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
github: https://github.com/MarvinTeichmann/KittiBox
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
Supercharge your Computer Vision models with the TensorFlow Object Detection API
blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
github: https://github.com/tensorflow/models/tree/master/object_detectionLocalization
****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
intro: CVPR 2016 oral
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
[图片上传中。。。（2）]
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
Ensemble of Part Detectors for Simultaneous Classification and Localization
https://arxiv.org/abs/1705.10034
Tutorials / Talks
****Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection**
slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf
Towards Good Practices for Recognition & Detection
intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf
Projects
****TensorBox: a simple framework for training neural networks to detect objects in images**
intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github: https://github.com/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
github: https://github.com/fmassa/object-detection.torch
Using DIGITS to train an Object Detection network
github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md
FCN-MultiBox Detector
intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
github: https://github.com/teaonly/FMD.torch
KittiBox: A car detection model implemented in Tensorflow.
keywords: MultiNet
intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
github: https://github.com/MarvinTeichmann/KittiBox
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
Supercharge your Computer Vision models with the TensorFlow Object Detection API
blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
github: https://github.com/tensorflow/models/tree/master/object_detectionLocalization
****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
intro: CVPR 2016 oral
arxiv: http://arxiv.org/abs/1511.07763
github: https://github.com/gidariss/LocNet
Learning Deep Features for Discriminative Localization
[图片上传中。。。（1）]
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
[图片上传中。。。（2）]
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
Ensemble of Part Detectors for Simultaneous Classification and Localization
https://arxiv.org/abs/1705.10034
Tutorials / Talks
****Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection**
slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf
Towards Good Practices for Recognition & Detection
intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf
Projects
****TensorBox: a simple framework for training neural networks to detect objects in images**
intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github: https://github.com/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
github: https://github.com/fmassa/object-detection.torch
Using DIGITS to train an Object Detection network
github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md
FCN-MultiBox Detector
intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
github: https://github.com/teaonly/FMD.torch
KittiBox: A car detection model implemented in Tensorflow.
keywords: MultiNet
intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
github: https://github.com/MarvinTeichmann/KittiBox
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
Supercharge your Computer Vision models with the TensorFlow Object Detection API
blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
github: https://github.com/tensorflow/models/tree/master/object_detection
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