Awesome List Updates on Jun 13, 2016

8 awesome lists updated today.

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1. Frontend Dev Bookmarks

Compatibility

• Keyboard: Working with keyboard input in a web browser.

• Web Accessibility: Web accessibility means that people with disabilities can perceive, understand, navigate, and interact with the Web, and that they can contribute to the Web.

Languages, Protocols, Browser APIs

• JavaScript Object Notation (JSON): JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language.

2. Awesome Unicode

One-To-Many Case Mappings / Wait a second... what did I just read?

• python-ftfy (⭐3.3k) - Given Unicode text, make its representation consistent and possibly less broken.

• vim-troll-stopper (⭐166) - Stop Unicode trolls from messing with your code.

Recursive HTML Tag Renaming Script / Wait a second... what did I just read?

• What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text

3. Awesome Deep Vision

Object Detection

• OverFeat, NYU [Paper]
  • OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, ICLR, 2014.

• R-CNN, UC Berkeley [Paper-CVPR14] [Paper-arXiv14]
  • Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.

• SPP, Microsoft Research [Paper]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.

• Fast R-CNN, Microsoft Research [Paper]
  • Ross Girshick, Fast R-CNN, arXiv:1504.08083.

• Faster R-CNN, Microsoft Research [Paper]
  • Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.

• R-CNN minus R, Oxford [Paper]
  • Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.

• End-to-end people detection in crowded scenes [Paper]
  • Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.

• Inside-Outside Net [Paper]
  • Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick, Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

• Deep Residual Network (Current State-of-the-Art) [Paper]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition

• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [Paper]

Object Tracking

• Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [Paper] [Code (⭐205)]

• Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [Paper] [Code (⭐131)]

• Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [Paper] [Code (⭐460)] [Project Page]

Low-Level Vision / Super-Resolution

• Very Deep Super-Resolution
  • Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015. [Paper]

• Deeply-Recursive Convolutional Network
  • Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [Paper]

• Casade-Sparse-Coding-Network
  • Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. [Paper] [Code]

• Perceptual Losses for Super-Resolution
  • Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016. [Paper] [Supplementary]

Low-Level Vision / Other Applications

• Optical Flow (FlowNet) [Paper]
  • Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.

• Blur Removal
  • Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444 [Paper]
  • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015 [Paper]

• Image Deconvolution [Web] [Paper]
  • Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.

• Deep Edge-Aware Filter [Paper]
  • Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.

• Computing the Stereo Matching Cost with a Convolutional Neural Network [Paper]
  • Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.

Edge Detection / Other Applications

• Holistically-Nested Edge Detection [Paper] [Code] (⭐1.6k)
  • Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.

• DeepEdge [Paper]
  • Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.

• DeepContour [Paper]
  • Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.

Semantic Segmentation / Other Applications

• Adelaide
  • Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [Paper] (1st ranked in VOC2012)
  • Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. [Paper] (4th ranked in VOC2012)

• Deep Parsing Network (DPN)
  • Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [Paper] (2nd ranked in VOC 2012)

• CentraleSuperBoundaries, INRIA [Paper]
  • Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)

• BoxSup [Paper]
  • Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)

• POSTECH
  • Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [Paper] (7th ranked in VOC2012)
  • Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [Paper]
  • Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 [Paper] [Project Page]

• Conditional Random Fields as Recurrent Neural Networks [Paper]
  • Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)

• DeepLab
  • Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. [Paper] (9th ranked in VOC2012)

• Zoom-out [Paper]
  • Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015

• Joint Calibration [Paper]
  • Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.

• Fully Convolutional Networks for Semantic Segmentation [Paper-CVPR15] [Paper-arXiv15]
  • Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.

• Hypercolumn [Paper]
  • Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.

• Deep Hierarchical Parsing
  • Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. [Paper]

• Learning Hierarchical Features for Scene Labeling [Paper-ICML12] [Paper-PAMI13]
  • Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.
  • Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.

• University of Cambridge [Web]
  • Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015. [Paper]

• Princeton
  • Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [Paper]

• Univ. of Washington, Allen AI
  • Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [Paper]

• INRIA
  • Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [Paper]

• UCSB
  • Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [Paper]

Visual Attention and Saliency / Other Applications

• Mr-CNN [Paper]
  • Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.

• Learning a Sequential Search for Landmarks [Paper]
  • Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.

• Multiple Object Recognition with Visual Attention [Paper]
  • Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.

• Recurrent Models of Visual Attention [Paper]
  • Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.

Object Recognition / Other Applications

• Weakly-supervised learning with convolutional neural networks [Paper]
  • Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.

• FV-CNN [Paper]
  • Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.

Understanding CNN / Other Applications

• Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015. [Paper]

Other Topics / Question Answering

• Surface Normal Estimation [Paper]
  • Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.

• Action Detection [Paper]
  • Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.

• Crowd Counting [Paper]
  • Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.

• 3D Shape Retrieval [Paper]
  • Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.

• Weakly-supervised Classification
  • Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, [Paper]

• Artistic Style [Paper] [Code] (⭐18k)
  • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, A Neural Algorithm of Artistic Style.

• Face Recognition
  • Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [Paper]
  • Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [Paper]
  • Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [Paper]

• Facial Landmark Detection
  • Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. [Paper] [Project]

Courses / Question Answering

• Deep Vision
  • [Stanford] CS231n: Convolutional Neural Networks for Visual Recognition
  • [CUHK] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)

• More Deep Learning
  • [Stanford] CS224d: Deep Learning for Natural Language Processing
  • [Oxford] Deep Learning by Prof. Nando de Freitas
  • [NYU] Deep Learning by Prof. Yann LeCun

Books / Question Answering

• Free Online Books
  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • Neural Networks and Deep Learning by Michael Nielsen
  • Deep Learning Tutorial by LISA lab, University of Montreal

Videos / Question Answering

• Talks
  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
  • Recent Developments in Deep Learning By Geoff Hinton
  • The Unreasonable Effectiveness of Deep Learning by Yann LeCun
  • Deep Learning of Representations by Yoshua bengio

Applications / Question Answering

• Adversarial Training
  • Code and hyperparameters for the paper "Generative Adversarial Networks" [Web] (⭐3.5k)

• Understanding and Visualizing
  • Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015. [Web] (⭐162)

• Semantic Segmentation
  • Source code for the paper "Rich feature hierarchies for accurate object detection and semantic segmentation," CVPR, 2014. [Web] (⭐2.3k)
  • Source code for the paper "Fully Convolutional Networks for Semantic Segmentation," CVPR, 2015. [Web] (⭐80)

• Super-Resolution
  • Image Super-Resolution for Anime-Style-Art [Web] (⭐25k)

• Edge Detection
  • Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015. [Web] (⭐89)
  • Source code for the paper "Holistically-Nested Edge Detection", ICCV 2015. [Web] (⭐1.6k)

4. Tips

Changes staged for commit

git diff --cached

Alternatives:

git diff --staged

Remove branches that have already been merged with master

git branch --merged master | grep -v '^\*' | xargs -n 1 git branch -d

Alternatives:

git branch --merged master | grep -v '^\*\|  master' | xargs -n 1 git branch -d # will not delete master if master is not checked out

See commit history for just the current branch

git cherry -v master

Turn off git colored terminal output

git config --global color.ui false

Specific color settings

git config --global <specific command e.g branch, diff> <true, false or always>

5. Engineering Blogs

Companies / S companies

• Slack https://slack.engineering/

6. Awesome Android

More lists of libraries / Custom Dialog

• Awesome Android @LibHunt - Your go-to Android Toolbox.

7. Awesome AutoHotkey

GUI / Combobox

• CbAutoComplete (⭐16) - by Pulover - Auto-completes typed values in an AHK ComboBox. Forum thread: link

8. Awesome Streaming

Table of Contents / Readings

• Real Time Analytics: Algorithms and Systems (VLDB 2015)

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