Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/eric-erki/awesome-action-recognition

A curated list of action recognition and related area resources
https://github.com/eric-erki/awesome-action-recognition
List: awesome-action-recognition
Last synced: 3 months ago
JSON representation
A curated list of action recognition and related area resources
Host: GitHub
URL: https://github.com/eric-erki/awesome-action-recognition
Owner: eric-erki
Created: 2020-02-02T10:38:18.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2020-02-02T10:38:39.000Z (almost 5 years ago)
Last Synced: 2024-04-22T01:49:49.208Z (7 months ago)
Size: 144 KB
Stars: 0
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
Awesome Lists containing this project

ultimate-awesome - awesome-action-recognition - A curated list of action recognition and related area resources. (Other Lists / PowerShell Lists)
README

        # Awesome Action Recognition: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of action recognition and related area (e.g. object recognition, pose estimation) resources, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).

## Contents

 - [Action Recognition and Video Understanding](#action-recognition-and-video-understanding)

 - [Object Recognition](#object-recognition)

 - [Pose Estimation](#pose-estimation)

 - [Competitions](#competitions)

## Action Recognition and Video Understanding

### Summary posts

* [Deep Learning for Videos: A 2018 Guide to Action Recognition](http://blog.qure.ai/notes/deep-learning-for-videos-action-recognition-review) - Summary of major landmark action recognition research papers till 2018

### Video Representation

* [Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition](https://papers.nips.cc/paper/8372-why-cant-i-dance-in-the-mall-learning-to-mitigate-scene-bias-in-action-recognition.pdf) - J. Choi et al., NeurIPS2019. [[project web]](http://chengao.vision/SDN/) [[code]](https://github.com/vt-vl-lab/SDN) [[arXiv]](https://arxiv.org/abs/1912.05534)

* [Large-scale weakly-supervised pre-training for video action recognition](https://research.fb.com/wp-content/uploads/2019/05/Large-scale-weakly-supervised-pre-training-for-video-action-recognition.pdf?) - D. Ghadiyaram et al., arXiv2019.

* [Video Classification with Channel-Separated Convolutional Networks](https://arxiv.org/pdf/1904.02811.pdf) - D. Tran et al., arXiv2019.

* [DistInit: Learning Video Representations without a Single Labeled Video](https://arxiv.org/pdf/1901.09244.pdf) - R. Girdhar et al., arXiv2019.

* [SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition](https://arxiv.org/pdf/1904.04289.pdf) - B. Korbar et al., arXiv2019.

* [Video Action Transformer Network](https://arxiv.org/pdf/1812.02707.pdf) - R. Girdhar et al., CVPR2019. [[project web]](https://rohitgirdhar.github.io/ActionTransformer/)

* [Learning Correspondence from the Cycle-consistency of Time](https://arxiv.org/pdf/1903.07593.pdf) - X. Wang et al., CVPR2019. [[code]](https://github.com/xiaolonw/TimeCycle) [[project web]](https://ajabri.github.io/timecycle/)

* [Representation Flow for Action Recognition](https://arxiv.org/pdf/1810.01455.pdf) - AJ. Piergiovanni and M. S. Ryoo et al., CVPR2019.

* [Collaborative Spatiotemporal Feature Learning for Video Action Recognition](https://arxiv.org/pdf/1903.01197.pdf) - C. Li et al., CVPR2019. 

* [Learning Video Representations from Correspondence Proposals](https://arxiv.org/pdf/1905.07853.pdf) - X. Liu et al., CVPR2019.

* [Timeception for Complex Action Recognition](https://arxiv.org/pdf/1812.01289.pdf) - N. Hussein et al., CVPR2019.

* [The Visual Centrifuge: Model-Free Layered Video Representations](https://arxiv.org/pdf/1812.01461.pdf) - J.-B. Alayrac et al., CVPR2019.

* [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/pdf/1812.05038.pdf) - C.-Y. Wu. et al., CVPR2019. [[code]](https://github.com/facebookresearch/video-long-term-feature-banks/)

* [Temporal Relational Reasoning in Videos](https://arxiv.org/pdf/1711.08496.pdf) - B. Zhou et al., ECCV2018. [[code]](https://github.com/metalbubble/TRN-pytorch) [[project web]](http://relation.csail.mit.edu/)

* [Action Recognition Zoo](https://github.com/coderSkyChen/Action_Recognition_Zoo) - 

Codes for popular action recognition models, written based on pytorch, verified on the something-something dataset.

* [Videos as Space-Time Region Graphs](https://arxiv.org/pdf/1806.01810.pdf) - X. Wang and A. Gupta, ECCV2018.

* [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hara_Can_Spatiotemporal_3D_CVPR_2018_paper.pdf) - K. Hara et al., CVPR2019. [[code]](https://github.com/kenshohara/3D-ResNets-PyTorch)

* [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/pdf/1711.11248.pdf) - D. Tran et al., CVPR2018. [[code]](https://github.com/facebookresearch/R2Plus1D) [[PyTorch]](https://github.com/irhumshafkat/R2Plus1D-PyTorch)

* [Attend and Interact: Higher-Order Object Interactions for Video Understanding](https://arxiv.org/abs/1711.06330) - CY. Ma et al., CVPR 2018.

* [Non-Local Neural Networks](https://arxiv.org/abs/1711.07971) - X. Wang et al., CVPR2018. [[code]](https://github.com/facebookresearch/video-nonlocal-net)

* [Rethinking Spatiotemporal Feature Learning For Video Understanding](https://arxiv.org/pdf/1712.04851.pdf) - S. Xie et al., arXiv2017. 

* [ConvNet Architecture Search for Spatiotemporal Feature Learning](https://arxiv.org/abs/1708.05038) - D. Tran et al, arXiv2017. Note: Aka Res3D. [[code]](https://github.com/facebook/C3D): In the repository, C3D-v1.1 is the Res3D implementation.

* [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](http://openaccess.thecvf.com/content_ICCV_2017/papers/Qiu_Learning_Spatio-Temporal_Representation_ICCV_2017_paper.pdf) - Z. Qui et al, ICCV2017. [[code]](https://github.com/ZhaofanQiu/pseudo-3d-residual-networks)

* [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/pdf/1705.07750.pdf) - J. Carreira et al, CVPR2017. [[code]](https://github.com/deepmind/kinetics-i3d)[[PyTorch code]](https://github.com/hassony2/kinetics_i3d_pytorch), [[another PyTorch code]](https://github.com/piergiaj/pytorch-i3d)

* [Learning Spatiotemporal Features with 3D Convolutional Networks](http://vlg.cs.dartmouth.edu/c3d/c3d_video.pdf) - D. Tran et al, ICCV2015. [[the official Caffe code]](https://github.com/facebook/C3D) [[project web]](http://vlg.cs.dartmouth.edu/c3d/) Note: Aka C3D. [[Python Wrapper]](https://github.com/chuckcho/C3D/tree/python-wrapper) Note that the official caffe does not support python wrapper. [[TensorFlow]](https://github.com/hx173149/C3D-tensorflow), [[TensorFlow + Keras]](https://github.com/axon-research/c3d-keras), [[Another TensorFlow Implemetation]](https://github.com/frankgu/C3D-tensorflow.git), [[Keras C3D Project web]](https://imatge.upc.edu/web/resources/c3d-model-keras-trained-over-sports-1m): [[Keras code]](https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2), [[Pretrained weights]](https://www.dropbox.com/s/ypiwalgtlrtnw8b/c3d-sports1M_weights.h5?dl=0).

* [Deep Temporal Linear Encoding Networks](https://arxiv.org/abs/1611.06678) - A. Diba et al, CVPR2017.

* [Temporal Convolutional Networks: A Unified Approach to Action Segmentation and Detection](https://arxiv.org/pdf/1611.05267.pdf) - C. Lea et al, CVPR 2017. [[code]](https://github.com/colincsl/TemporalConvolutionalNetworks)

* [Long-term Temporal Convolutions](https://arxiv.org/pdf/1604.04494v1.pdf) - G. Varol et al, TPAMI2017. [[project web]](http://www.di.ens.fr/willow/research/ltc/) [[code]](https://github.com/gulvarol/ltc) 

* [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/pdf/1608.00859.pdf) - L. Wang et al, arXiv 2016. [[code]](https://github.com/yjxiong/temporal-segment-networks)

* [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://arxiv.org/pdf/1604.06573.pdf) - C. Feichtenhofer et al, CVPR2016. [[code]](https://github.com/feichtenhofer/twostreamfusion)

* [Two-Stream Convolutional Networks for Action Recognition in Videos](http://www.robots.ox.ac.uk/~vgg/publications/2014/Simonyan14b/simonyan14b.pdf.pdf) - K. Simonyan and A. Zisserman, NIPS2014.

#### Useful Code Repos on Video Representation Learning

* [[3D ResNet PyTorch]](https://github.com/kenshohara/3D-ResNets-PyTorch)

* [[PyTorch Video Research]](https://github.com/gsig/PyVideoResearch)

* [[M-PACT: Michigan Platform for Activity Classification in Tensorflow]](https://github.com/MichiganCOG/M-PACT)

* [[Inflated models on PyTorch]](https://github.com/hassony2/inflated_convnets_pytorch)

* [[I3D models transfered from Tensorflow to PyTorch]](https://github.com/hassony2/kinetics_i3d_pytorch)

* [[A Two Stream Baseline on Kinectics dataset]](https://github.com/gurkirt/2D-kinectics)

* [[MMAction]](https://github.com/open-mmlab/mmaction)

### Action Classification

* [Neural Graph Matching Networks for Fewshot 3D Action Recognition](http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf) - M. Guo et al., ECCV2018.

* [Temporal 3D ConvNets using Temporal Transition Layer](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w19/Diba_Temporal_3D_ConvNets_CVPR_2018_paper.pdf) - A. Diba et al., CVPRW2018.

* [Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification](https://arxiv.org/abs/1711.08200) - A. Diba et al., arXiv2017.

* [Attentional Pooling for Action Recognition](https://arxiv.org/abs/1711.01467) - R. Girdhar and D. Ramanan, NIPS2017. [[code]](https://github.com/rohitgirdhar/AttentionalPoolingAction)

* [Fully Context-Aware Video Prediction](https://arxiv.org/pdf/1710.08518v1.pdf) - Byeon et al, arXiv2017.

* [Hidden Two-Stream Convolutional Networks for Action Recognition](https://arxiv.org/pdf/1704.00389.pdf) - Y. Zhu et al, arXiv2017. [[code]](https://github.com/bryanyzhu/Hidden-Two-Stream)

* [Dynamic Image Networks for Action Recognition](https://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16a/bilen16a.pdf) - H. Bilen et al, CVPR2016. [[code]](https://github.com/hbilen/dynamic-image-nets) [[project web]](http://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16a/)

* [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf) - J. Donahue et al, CVPR2015. [[code]](https://github.com/LisaAnne/lisa-caffe-public/tree/lstm_video_deploy) [[project web]](http://jeffdonahue.com/lrcn/)

* [Describing Videos by Exploiting Temporal Structure](http://arxiv.org/pdf/1502.08029v4.pdf) - L. Yao et al, ICCV2015. [[code]](https://github.com/yaoli/arctic-capgen-vid) note: from the same group of RCN paper “Delving Deeper into Convolutional Networks for Learning Video Representations"

* [Two-Stream SR-CNNs for Action Recognition in Videos](http://wanglimin.github.io/papers/ZhangWWQW_CVPR16.pdf) - L. Wang et al, BMVC2016.

* [Real-time Action Recognition with Enhanced Motion Vector CNNs](http://arxiv.org/abs/1604.07669) - B. Zhang et al, CVPR2016. [[code]](https://github.com/zbwglory/MV-release)

* [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Action_Recognition_With_2015_CVPR_paper.pdf) - L. Wang et al, CVPR2015. [[code]](https://github.com/wanglimin/TDD)

### Skeleton-Based Action Classification

* [Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition](http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Actional-Structural_Graph_Convolutional_Networks_for_Skeleton-Based_Action_Recognition_CVPR_2019_paper.html) - M. Li et al., CVPR2019.

* [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://arxiv.org/abs/1902.09130) - C. Si et al., CVPR2019.

* [View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition](https://ieeexplore.ieee.org/abstract/document/8630687) - P. Zhang et al., TPAMI2019.

* [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135) - S. Yan et al., AAAI2018. [[code]](https://github.com/yysijie/st-gcn)

* [Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition](http://openaccess.thecvf.com/content_cvpr_2018/html/Tang_Deep_Progressive_Reinforcement_CVPR_2018_paper.html) - Y. Tang et al., CVPR2018.

* [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://dl.acm.org/citation.cfm?id=3304527) - C. Li et al., IJCAI2018.

* [Part-based Graph Convolutional Network for Action Recognition](http://bmvc2018.org/contents/papers/1003.pdf) - K. Thakkar et al., BMVC2018.

### Temporal Action Detection

* [Rethinking the Faster R-CNN Architecture for Temporal Action Localization](https://arxiv.org/pdf/1804.07667v1.pdf) - Yu-Wei Chao et al., CVPR2018

* [Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://arxiv.org/pdf/1712.05080) - Phuc Nguyen et al., CVPR 2018

* [Temporal Deformable Residual Networks for Action Segmentation in Videos](http://web.engr.oregonstate.edu/~sinisa/research/publications/cvpr18_TDRN.pdf) - P. Lei and S. Todrovic., CVPR2018.

* [End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos](http://vision.stanford.edu/pdf/buch2017bmvc.pdf) - Shayamal Buch et al., BMVC 2017 [[code]](https://github.com/shyamal-b/ss-tad)

* [Cascaded Boundary Regression for Temporal Action Detection](https://arxiv.org/abs/1705.01180) - Jiyang Gao et al., BMVC 2017 [[code](https://github.com/jiyanggao/CBR)]

* [Temporal Tessellation: A Unified Approach for Video Analysis](http://openaccess.thecvf.com/content_ICCV_2017/papers/Kaufman_Temporal_Tessellation_A_ICCV_2017_paper.pdf) - Kaufman et al., ICCV2017. [[code]](https://github.com/dot27/temporal-tessellation) 

* [Temporal Action Detection with Structured Segment Networks](http://cn.arxiv.org/pdf/1704.06228v2) - Y. Zhao et al., ICCV2017. [[code]](https://github.com/yjxiong/action-detection) [[project web]](http://yjxiong.me/others/ssn/)

* [Temporal Context Network for Activity Localization in Videos](https://arxiv.org/pdf/1708.02349.pdf) - X. Dai et al., ICCV2017.

* [Detecting the Moment of Completion: Temporal Models for Localising Action Completion](https://arxiv.org/abs/1710.02310) - F. Heidarivincheh et al., arXiv2017.

* [CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos](https://arxiv.org/abs/1703.01515/) - Z. Shou et al, CVPR2017. [[code]](https://bitbucket.org/columbiadvmm/cdc)

* [SST: Single-Stream Temporal Action Proposals](http://vision.stanford.edu/pdf/buch2017cvpr.pdf) - S. Buch et al, CVPR2017. [[code]](https://github.com/shyamal-b/sst)

* [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://arxiv.org/abs/1703.07814) - H. Xu et al, arXiv2017. [[code]](https://github.com/VisionLearningGroup/R-C3D) [[project web]](http://ai.bu.edu/r-c3d/) [[PyTorch]](https://github.com/sunnyxiaohu/R-C3D.pytorch)

* [DAPs: Deep Action Proposals for Action Understanding](https://ivul.kaust.edu.sa/Documents/Publications/2016/DAPs%20Deep%20Action%20Proposals%20for%20Action%20Understanding.pdf) - V. Escorcia et al, ECCV2016. [[code]](https://github.com/escorciav/daps) [[raw data]](https://github.com/escorciav/daps)

* [Online Action Detection using Joint Classification-Regression Recurrent Neural Networks](https://arxiv.org/abs/1604.05633) - Y. Li et al, ECCV2016. Noe: RGB-D Action Detection

* [Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs](http://dvmmweb.cs.columbia.edu/files/dvmm_scnn_paper.pdf) - Z. Shou et al, CVPR2016. [[code]](https://github.com/zhengshou/scnn) Note: Aka S-CNN.

* [Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Heilbron_Fast_Temporal_Activity_CVPR_2016_paper.pdf) - F. Heilbron et al, CVPR2016. [[code]](https://github.com/cabaf/sparseprop) Note: Depends on [C3D](http://vlg.cs.dartmouth.edu/c3d/), aka SparseProp.

* [Actionness Estimation Using Hybrid Fully Convolutional Networks](https://arxiv.org/abs/1604.07279) - L. Wang et al, CVPR2016. [[code]](https://github.com/wanglimin/actionness-estimation/) Note: The code is not a complete verision. It only contains a demo, not training. [[project web]](http://wanglimin.github.io/actionness_hfcn/index.html)

* [Learning Activity Progression in LSTMs for Activity Detection and Early Detection](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ma_Learning_Activity_Progression_CVPR_2016_paper.pdf) - S. Ma et al, CVPR2016.

* [End-to-end Learning of Action Detection from Frame Glimpses in Videos](http://vision.stanford.edu/pdf/yeung2016cvpr.pdf) - S. Yeung et al, CVPR2016. [[code]](https://github.com/syyeung/frameglimpses) [[project web]](http://ai.stanford.edu/~syyeung/frameglimpses.html) Note: This method uses reinforcement learning

* [Fast Action Proposals for Human Action Detection and Search](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Yu_Fast_Action_Proposals_2015_CVPR_paper.pdf) - G. Yu and J. Yuan, CVPR2015. Note: code for FAP is NOT available online. Note: Aka FAP.

* [Bag-of-fragments: Selecting and encoding video fragments for event detection and recounting](https://staff.fnwi.uva.nl/t.e.j.mensink/publications/mettes15icmr.pdf) - P. Mettes et al, ICMR2015.

* [Action localization in videos through context walk](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Soomro_Action_Localization_in_ICCV_2015_paper.pdf) - K. Soomro et al, ICCV2015.

### Spatio-Temporal Action Detection

* [A Better Baseline for AVA](https://arxiv.org/pdf/1807.10066.pdf) - R. Girdhar et al., ActivityNet Workshop, CVPR2018. 

* [Real-Time End-to-End Action Detection with Two-Stream Networks](https://arxiv.org/abs/1802.08362) - A. El-Nouby and G. Taylor, arXiv2018.

* [Human Action Localization with Sparse Spatial Supervision](https://arxiv.org/pdf/1605.05197.pdf) - P. Weinzaepfel et al., arXiv2017. 

* [Unsupervised Action Discovery and Localization in Videos](http://openaccess.thecvf.com/content_ICCV_2017/papers/Soomro_Unsupervised_Action_Discovery_ICCV_2017_paper.pdf) - K. Soomro and M. Shah, ICCV2017.

* [Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions](https://arxiv.org/pdf/1707.09145.pdf) - P. Mettes and C. G. M. Snoek, ICCV2017.

* [Action Tubelet Detector for Spatio-Temporal Action Localization](https://arxiv.org/abs/1705.01861) - V. Kalogeiton et al, ICCV2017. [[code]](https://github.com/vkalogeiton/caffe/tree/act-detector) [[project web]](http://thoth.inrialpes.fr/src/ACTdetector/)

* [Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos](https://arxiv.org/pdf/1703.10664.pdf) - [R. Hou](http://www.cs.ucf.edu/~rhou/) et al, ICCV2017. [[project web]](http://crcv.ucf.edu/projects/TCNN/)

* [Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection](https://arxiv.org/abs/1704.00616) - M. Zolfaghari et al, ICCV2017. [[project web]](https://lmb.informatik.uni-freiburg.de/projects/action_chain/)

* [TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_TORNADO_A_Spatio-Temporal_ICCV_2017_paper.pdf) - H. Zhu et al., ICCV2017. 

* [Online Real time Multiple Spatiotemporal Action Localisation and Prediction](https://arxiv.org/pdf/1611.08563v3.pdf) - [G. Singh](http://gurkirt.github.io/) et al, ICCV2017. [[code]](https://github.com/gurkirt/realtime-action-detection)

* [AMTnet: Action-Micro-Tube regression by end-to-end trainable deep architecture](https://arxiv.org/pdf/1704.04952.pdf) - S. Saha et al, ICCV2017.

* [Am I Done? Predicting Action Progress in Videos](https://arxiv.org/abs/1705.01781) - F. Becattini et al, BMVC2017.

* [Generic Tubelet Proposals for Action Localization](https://arxiv.org/abs/1705.10861) - J. He et al, arXiv2017.

* [Incremental Tube Construction for Human Action Detection](https://arxiv.org/pdf/1704.01358.pdf) - H. S. Behl et al, arXiv2017.

* [Multi-region two-stream R-CNN for action detection](https://www.robots.ox.ac.uk/~vgg/rg/papers/peng16eccv.pdf) - [X. Peng](http://xjpeng.weebly.com/) and C. Schmid. ECCV2016. [[code]](https://github.com/pengxj/action-faster-rcnn)

* [Spot On: Action Localization from Pointly-Supervised Proposals](http://jvgemert.github.io/pub/spotOnECCV16.pdf) - P. Mettes et al, ECCV2016.

* [Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos](https://arxiv.org/abs/1608.01529) - S. Saha et al, BMVC2016. [[code]](https://bitbucket.org/sahasuman/bmvc2016_code) [[project web]](http://sahasuman.bitbucket.org/bmvc2016/)

* [Learning to track for spatio-temporal action localization](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Weinzaepfel_Learning_to_Track_ICCV_2015_paper.pdf) - P. Weinzaepfel et al. ICCV2015.

* [Action detection by implicit intentional motion clustering](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chen_Action_Detection_by_ICCV_2015_paper.pdf) - W. Chen and J. Corso, ICCV2015.

* [Finding Action Tubes](https://people.eecs.berkeley.edu/~gkioxari/ActionTubes/action_tubes.pdf) - G. Gkioxari and J. Malik CVPR2015. [[code]](https://github.com/gkioxari/ActionTubes) [[project web]](https://people.eecs.berkeley.edu/~gkioxari/ActionTubes/)

* [APT: Action localization proposals from dense trajectories](http://jvgemert.github.io/pub/gemertBMVC15APTactionProposals.pdf) - J. Gemert et al, BMVC2015. [[code]](https://github.com/jvgemert/apt)

* [Spatio-Temporal Object Detection Proposals](https://hal.inria.fr/hal-01021902/PDF/proof.pdf) - D. Oneata et al, ECCV2014. [[code]](https://bitbucket.org/doneata/proposals) [[project web]](http://lear.inrialpes.fr/~oneata/3Dproposals/)

* [Action localization with tubelets from motion](http://isis-data.science.uva.nl/cgmsnoek/pub/jain-tubelets-cvpr2014.pdf) - M. Jain et al, CVPR2014.

* [Spatiotemporal deformable part models for action detection](http://crcv.ucf.edu/papers/cvpr2013/cvpr2013-sdpm.pdf) - [Y. Tian](http://www.cs.ucf.edu/~ytian/index.html) et al, CVPR2013. [[code]](http://www.cs.ucf.edu/~ytian/sdpm.html)

* [Action localization in videos through context walk](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Soomro_Action_Localization_in_ICCV_2015_paper.pdf) - K. Soomro et al, ICCV2015.

* [Fast Action Proposals for Human Action Detection and Search](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Yu_Fast_Action_Proposals_2015_CVPR_paper.pdf) - G. Yu and J. Yuan, CVPR2015. Note: code for FAP is NOT available online. Note: Aka FAP.

### Ego-Centric Action Recognition

* [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://arxiv.org/pdf/1804.09627.pdf) - G. Sigurdsson et al., CVPR2018. [[code]](https://github.com/gsig/actor-observer)

### Miscellaneous

* [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://arxiv.org/pdf/1904.04346.pdf) - P. Parma and B. T. Morris. CVPR2019.

* [PathTrack: Fast Trajectory Annotation with Path Supervision](http://openaccess.thecvf.com/content_ICCV_2017/papers/Manen_PathTrack_Fast_Trajectory_ICCV_2017_paper.pdf) - S. Manen et al., ICCV2017.

* [CortexNet: a Generic Network Family for Robust Visual Temporal Representations](https://arxiv.org/pdf/1706.02735.pdf) A. Canziani and E. Culurciello - arXiv2017. [[code]](https://github.com/atcold/pytorch-CortexNet) [[project web]](https://engineering.purdue.edu/elab/CortexNet/)

* [Slicing Convolutional Neural Network for Crowd Video Understanding](http://www.ee.cuhk.edu.hk/~jshao/papers_jshao/jshao_cvpr16_scnn.pdf) - J. Shao et al, CVPR2016. [[code]](https://github.com/amandajshao/Slicing-CNN)

* [Two-Stream (RGB and Flow) pretrained model weights](https://github.com/craftGBD/caffe-GBD/tree/master/models/action_recognition)

### Action Recognition Datasets

* [Video Dataset Overview from Antoine Miech](https://www.di.ens.fr/~miech/datasetviz/)

* [HACS](http://hacs.csail.mit.edu/) 

* [Moments in Time](http://moments.csail.mit.edu/), [paper](http://moments.csail.mit.edu/data/moments_paper.pdf)

* [AVA](https://research.google.com/ava/), [paper](https://arxiv.org/abs/1705.08421), [[INRIA web]](http://thoth.inrialpes.fr/ava/getava.php) for missing videos

* [Kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/), [paper](https://arxiv.org/pdf/1705.07750.pdf), [download toolkit](https://github.com/activitynet/ActivityNet/tree/master/Crawler/Kinetics)

* [OOPS](https://oops.cs.columbia.edu/data/) - A dataset of unintentional action, [paper](https://arxiv.org/abs/1911.11206)

* [COIN](https://coin-dataset.github.io/) - a large-scale dataset for comprehensive instructional video analysis, [paper](https://arxiv.org/abs/1903.02874)

* [YouTube-8M](https://research.google.com/youtube8m/), [technical report](https://arxiv.org/abs/1609.08675)

* [YouTube-BB](https://research.google.com/youtube-bb/), [technical report](https://arxiv.org/pdf/1702.00824.pdf)

* [DALY](http://thoth.inrialpes.fr/daly/) Daily Action Localization in Youtube videos. Note: Weakly supervised action detection dataset. Annotations consist of start and end time of each action, one bounding box per each action per video.

* [20BN-JESTER](https://www.twentybn.com/datasets/jester), [20BN-SOMETHING-SOMETHING](https://www.twentybn.com/datasets/something-something)

* [ActivityNet](http://activity-net.org/) Note: They provide a download script and evaluation code [here](https://github.com/activitynet) .

* [Charades](http://allenai.org/plato/charades/)

* [Charades-Ego](https://prior.allenai.org/projects/charades-ego), [paper](https://arxiv.org/pdf/1804.09626.pdf) - First person and third person video aligned dataset

* [EPIC-Kitchens](https://epic-kitchens.github.io/), [paper](https://arxiv.org/abs/1804.02748) - First person videos recorded in kitchens. Note they provide download scripts and a python library [here](https://github.com/epic-kitchens)

* [Sports-1M](http://cs.stanford.edu/people/karpathy/deepvideo/classes.html) - Large scale action recognition dataset.

* [THUMOS14](http://crcv.ucf.edu/THUMOS14/) Note: It overlaps with [UCF-101](http://crcv.ucf.edu/data/UCF101.php) dataset. 

* [THUMOS15](http://www.thumos.info/home.html) Note: It overlaps with [UCF-101](http://crcv.ucf.edu/data/UCF101.php) dataset.

* [HOLLYWOOD2](http://www.di.ens.fr/~laptev/actions/hollywood2/): [Spatio-Temporal annotations](https://staff.fnwi.uva.nl/p.s.m.mettes/index.html#data)

* [UCF-101](http://crcv.ucf.edu/data/UCF101.php), [annotation provided by THUMOS-14](http://crcv.ucf.edu/ICCV13-Action-Workshop/index.files/UCF101_24Action_Detection_Annotations.zip), and [corrupted annotation list](https://github.com/jinwchoi/Jinwoo-Computer-Vision-and-Machine-Learing-papers-to-read/blob/master/UCF101_Spatial_Annotation_Corrupted_file_list),  [UCF-101 corrected annotations](https://github.com/gurkirt/corrected-UCF101-Annots) and [different version annotaions](https://github.com/jvgemert/apt). And there are also some pre-computed spatiotemporal action detection [results](https://drive.google.com/drive/folders/0B-LzM05qEdk0aG5pTE94VFI1SUk)

* [UCF-50](http://crcv.ucf.edu/data/UCF50.php).

* [UCF-Sports](http://crcv.ucf.edu/data/UCF_Sports_Action.php), note: the train/test split link in the official website is broken. Instead, you can download it from [here](http://pascal.inrialpes.fr/data2/oneata/data/ucf_sports/videos.txt).

* [HMDB](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/)

* [J-HMDB](http://jhmdb.is.tue.mpg.de/)

* [LIRIS-HARL](http://liris.cnrs.fr/voir/activities-dataset/)

* [KTH](http://www.nada.kth.se/cvap/actions/)

* [MSR Action](https://www.microsoft.com/en-us/download/details.aspx?id=52315) Note: It overlaps with [KTH](http://www.nada.kth.se/cvap/actions/) datset.

* [Sports Videos in the Wild](http://cvlab.cse.msu.edu/project-svw.html)

* [NTU RGB+D](https://github.com/shahroudy/NTURGB-D)

* [Mixamo Mocap Dataset](http://mocap.cs.cmu.edu/)

* [UWA3D Multiview Activity II Dataset](http://staffhome.ecm.uwa.edu.au/~00053650/databases.html)

* [Northwestern-UCLA Dataset](https://users.eecs.northwestern.edu/~jwa368/my_data.html)

* [SYSU 3D Human-Object Interaction Dataset](http://www.isee-ai.cn/~hujianfang/ProjectJOULE.html)

* [MEVA (Multiview Extended Video with Activities) Dataset](http://mevadata.org)

### Video Annotation

* [Efficiently scaling up crowdsourced video annotation](http://cvrr.ucsd.edu/ece285/Spring2014/papers/Vondrick_IJCV2013.pdf) - C. Vondrick et. al, IJCV2013. [[code]](https://github.com/cvondrick/vatic)

* [The Design and Implementation of ViPER](https://www.cs.umd.edu/grad/scholarlypapers/papers/davidm-viper.pdf) - D. Mihalcik and D. Doermann, Technical report.

* [VTT: Visual Object Tagging Tool](https://github.com/microsoft/VoTT). Modern app to annotate objects in videos and images. It facilitates the development of an end-to-end machine learning pipeline encompassing the annotation/export/import of assets. Moreover, it could run as a native app or via web.

* [VIA: VGG Image Annotator](http://www.robots.ox.ac.uk/~vgg/software/via/). Simple and standalone manual annotation web-app for image, audio and video. It runs in the web browser and does not require any installation or setup.

## Object Recognition

### Object Detection

* [Deformable Convolutional Networks](http://openaccess.thecvf.com/content_ICCV_2017/papers/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.pdf) - J. Dai et al., ICCV2017. [[official code]](https://github.com/msracver/Deformable-ConvNets)

* [Detectron](https://github.com/facebookresearch/Detectron) - Open Source Object Detection Framework from Facebook AI Research. Includes Mask R-CNN, FPN, and etc. Caffe2 implementation.

* [Mask R-CNN](https://arxiv.org/abs/1703.06870) - K. He et al, [[Detectron]](https://github.com/facebookresearch/Detectron), [[TensorFlow + Keras]](https://github.com/matterport/Mask_RCNN), [[MXNet]](https://github.com/TuSimple/mx-maskrcnn), [[TensorFlow]](https://github.com/CharlesShang/FastMaskRCNN), [[PyTorch]](https://github.com/felixgwu/mask_rcnn_pytorch) - State-of-the-art object detection/instance segmentation algorithm.

* [Faster R-CNN](https://arxiv.org/abs/1506.01497) - S. Ren et al, NIPS2015. [[official MatCaffe code]](https://github.com/ShaoqingRen/faster_rcnn), [[PyCaffe]](https://github.com/rbgirshick/py-faster-rcnn), [[TensorFlow]](https://github.com/smallcorgi/Faster-RCNN_TF), [[Another TF implementation]](https://github.com/CharlesShang/TFFRCNN) [[Keras]](https://github.com/yhenon/keras-frcnn) - State-of-the-art object detector.

* [YOLO](https://pjreddie.com/media/files/papers/yolo.pdf) - J. Redmon et al, CVPR2016. [[official code]](https://github.com/pjreddie/darknet.git), [[TensorFLow]](https://github.com/gliese581gg/YOLO_tensorflow) - Fast object detector.

* [YOLO9000](https://arxiv.org/abs/1612.08242) - J. Redmon and A. Farhadi, CVPR2017. [[official code]](https://pjreddie.com/darknet/yolo/) - State-of-the-art object detector which can detect 9000 objects in realtime.

* [SSD](https://arxiv.org/abs/1512.02325) - W. Liu et al, ECCV2016. [[official PyCaffe code]](https://github.com/weiliu89/caffe/tree/ssd), [[TensorFlow]](https://github.com/balancap/SSD-Tensorflow), [[Keras]](https://github.com/rykov8/ssd_keras) - State-of-the-art object detector with realtime processing speed.

* [RetinaNet](https://arxiv.org/abs/1708.02002) - Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár, Facebook AI Research FAIR & ICCV 2017.[[Keras]](https://github.com/fizyr/keras-retinanet) - State-of-the-art object detector with realtime processing speed.

### Video Object Detection

* [Detect to Track and Track to Detect] - C. Feichtenhofer et al., ICCV2017. [[code]](https://github.com/feichtenhofer/detect-track), [[project web]](http://www.robots.ox.ac.uk/~vgg/research/detect-track/)

* [Flow-Guided Feature Aggregation for Video Object Detection] - X. Zhu et al., ICCV2017. [[code]](https://github.com/msracver/Flow-Guided-Feature-Aggregation), aka FGFA

### Video Object Detection Datasets

* [ImageNet VID](http://image-net.org/challenges/LSVRC/2017/download-images-1p39.php)

* [YouTube-8M](https://research.google.com/youtube8m/), [technical report](https://arxiv.org/abs/1609.08675)

* [YouTube-BB](https://research.google.com/youtube-bb/), [technical report](https://arxiv.org/pdf/1702.00824.pdf)

## Pose Estimation

### Pose Estimation

* [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) - PyTorch based realtime and accurate pose estimation and tracking tool from SJTU.

* [Detect-and-Track: Efficient Pose Estimation in Videos](https://arxiv.org/abs/1712.09184) - R. Girdhar et al., arXiv2017.

* [OpenPose Library](https://github.com/CMU-Perceptual-Computing-Lab/openpose) - Caffe based realtime pose estimation library from CMU.

* [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1611.08050) - Z. Cao et al, CVPR2017. [[code]](https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation) depends on the [[caffe RT pose]](https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose.git) - Earlier version of OpenPose from CMU.

* [DensePose](https://arxiv.org/abs/1802.00434v1) [[code]](https://github.com/facebookresearch/DensePose) - Dense pose human estimation in the wild implemented in the Detectron framework.

* [MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network](https://arxiv.org/abs/1807.04067) - M. Kocabas et al, ECCV2018. [[code]](https://github.com/salihkaragoz/pose-residual-network-pytorch)

## Competitions

### Competitions

* [ActEV (Activities in Extended Video](https://actev.nist.gov/sdl) - Activity detection in security camera videos. Runs through 2021. Hosted by NIST.

## Licenses

License

[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, [Jinwoo Choi](https://sites.google.com/site/jchoivision/) has waived all copyright and related or neighboring rights to this work.

## Contributing

Please read the [contribution guidelines](contributing.md). Then please feel free to send me [pull requests](https://github.com/jinwchoi/Action-Recognition/pulls) or email ([email protected]) to add links.