Awesome Action Recognition: / Action Recognition and Video Understanding / Summary posts |
| Deep Learning for Videos: A 2018 Guide to Action Recognition | | | Summary of major landmark action recognition research papers till 2018 |
| Literature Survey: Human Action Recognition | | | Brief human action recognition literature survey of work published between 2014 and 2019 |
Awesome Action Recognition: / Action Recognition and Video Understanding / Video Representation |
| Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition | | | J. Choi et al., NeurIPS2019 |
| SlowFast Networks for Video Recognition | | | C. Feichtenhofer et al., ICCV2019 |
| Large-scale weakly-supervised pre-training for video action recognition | | | D. Ghadiyaram et al., arXiv2019 |
| Video Classification with Channel-Separated Convolutional Networks | | | D. Tran et al., arXiv2019 |
| DistInit: Learning Video Representations without a Single Labeled Video | | | R. Girdhar et al., arXiv2019 |
| SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition | | | B. Korbar et al., arXiv2019 |
| Video Action Transformer Network | | | R. Girdhar et al., CVPR2019 |
| Learning Correspondence from the Cycle-consistency of Time | | | X. Wang et al., CVPR2019 |
| Representation Flow for Action Recognition | | | AJ. Piergiovanni and M. S. Ryoo et al., CVPR2019 |
| Collaborative Spatiotemporal Feature Learning for Video Action Recognition | | | C. Li et al., CVPR2019 |
| Learning Video Representations from Correspondence Proposals | | | X. Liu et al., CVPR2019 |
| Timeception for Complex Action Recognition | | | N. Hussein et al., CVPR2019 |
| The Visual Centrifuge: Model-Free Layered Video Representations | | | J.-B. Alayrac et al., CVPR2019 |
| Long-Term Feature Banks for Detailed Video Understanding | | | C.-Y. Wu. et al., CVPR2019 |
| Temporal Relational Reasoning in Videos | | | B. Zhou et al., ECCV2018 |
| Action Recognition Zoo | 244 | over 7 years ago | -
Codes for popular action recognition models, written based on pytorch, verified on the something-something dataset |
| Videos as Space-Time Region Graphs | | | X. Wang and A. Gupta, ECCV2018 |
| Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? | | | K. Hara et al., CVPR2019 |
| A Closer Look at Spatiotemporal Convolutions for Action Recognition | | | D. Tran et al., CVPR2018 |
| Attend and Interact: Higher-Order Object Interactions for Video Understanding | | | CY. Ma et al., CVPR 2018 |
| Non-Local Neural Networks | | | X. Wang et al., CVPR2018 |
| Rethinking Spatiotemporal Feature Learning For Video Understanding | | | S. Xie et al., arXiv2017 |
| ConvNet Architecture Search for Spatiotemporal Feature Learning | | | D. Tran et al, arXiv2017. Note: Aka Res3D. : In the repository, C3D-v1.1 is the Res3D implementation |
| Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks | | | Z. Qui et al, ICCV2017 |
| Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset | | | J. Carreira et al, CVPR2017. , |
| Learning Spatiotemporal Features with 3D Convolutional Networks | | | D. Tran et al, ICCV2015. Note: Aka C3D. Note that the official caffe does not support python wrapper. , , , : , |
| Deep Temporal Linear Encoding Networks | | | A. Diba et al, CVPR2017 |
| Temporal Convolutional Networks: A Unified Approach to Action Segmentation and Detection | | | C. Lea et al, CVPR 2017 |
| Long-term Temporal Convolutions | | | G. Varol et al, TPAMI2017 |
| Temporal Segment Networks: Towards Good Practices for Deep Action Recognition | | | L. Wang et al, arXiv 2016 |
| Convolutional Two-Stream Network Fusion for Video Action Recognition | | | C. Feichtenhofer et al, CVPR2016 |
| Two-Stream Convolutional Networks for Action Recognition in Videos | | | K. Simonyan and A. Zisserman, NIPS2014 |
| Temporal Recurrent Networks for Online Action Detection | | | M. Xu et al, ICCV2019 |
| Long Short-Term Transformer for Online Action Detection | | | M. Xu et al, Neurips2021 |
| [3D ResNet PyTorch] | 3,920 | over 5 years ago | |
| [PyTorch Video Research] | 533 | over 7 years ago | |
| [M-PACT: Michigan Platform for Activity Classification in Tensorflow] | 107 | over 7 years ago | |
| [Inflated models on PyTorch] | 148 | over 5 years ago | |
| [I3D models transfered from Tensorflow to PyTorch] | 532 | over 2 years ago | |
| [A Two Stream Baseline on Kinectics dataset] | 42 | over 7 years ago | |
| [MMAction] | 1,863 | over 4 years ago | |
| [MMAction2] | 4,360 | about 2 years ago | |
| [PySlowFast] | 6,680 | almost 2 years ago | |
| [Decord] | 1,923 | about 2 years ago | Efficient video reader for python |
| [I3D models converted from Tensorflow to Core ML] | 24 | about 6 years ago | |
| [Extract frame and optical-flow from videos, #docker] | 133 | about 4 years ago | |
| [NVIDIA-DALI, video loading pipelines] | | | |
| [NVIDIA optical-flow SDK] | | | |
Awesome Action Recognition: / Action Recognition and Video Understanding / Action Classification |
| Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with Autism | | | P. Pandey et al, AAAI 2020 |
| Neural Graph Matching Networks for Fewshot 3D Action Recognition | | | M. Guo et al., ECCV2018 |
| Temporal 3D ConvNets using Temporal Transition Layer | | | A. Diba et al., CVPRW2018 |
| Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification | | | A. Diba et al., arXiv2017 |
| Attentional Pooling for Action Recognition | | | R. Girdhar and D. Ramanan, NIPS2017 |
| Fully Context-Aware Video Prediction | | | Byeon et al, arXiv2017 |
| Hidden Two-Stream Convolutional Networks for Action Recognition | | | Y. Zhu et al, arXiv2017 |
| Dynamic Image Networks for Action Recognition | | | H. Bilen et al, CVPR2016 |
| Long-term Recurrent Convolutional Networks for Visual Recognition and Description | | | J. Donahue et al, CVPR2015 |
| Describing Videos by Exploiting Temporal Structure | | | L. Yao et al, ICCV2015. 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 | | | L. Wang et al, BMVC2016 |
| Real-time Action Recognition with Enhanced Motion Vector CNNs | | | B. Zhang et al, CVPR2016 |
| Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors | | | L. Wang et al, CVPR2015 |
Awesome Action Recognition: / Action Recognition and Video Understanding / Skeleton-Based Action Classification |
| Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition | | | M. Li et al., CVPR2019 |
| An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition | | | C. Si et al., CVPR2019 |
| View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition | | | P. Zhang et al., TPAMI2019 |
| Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition | | | S. Yan et al., AAAI2018 |
| Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition | | | Y. Tang et al., CVPR2018 |
| Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation | | | C. Li et al., IJCAI2018 |
| Part-based Graph Convolutional Network for Action Recognition | | | K. Thakkar et al., BMVC2018 |
Awesome Action Recognition: / Action Recognition and Video Understanding / Temporal Action Detection |
| Rethinking the Faster R-CNN Architecture for Temporal Action Localization | | | Yu-Wei Chao et al., CVPR2018 |
| Weakly Supervised Action Localization by Sparse Temporal Pooling Network | | | Phuc Nguyen et al., CVPR 2018 |
| Temporal Deformable Residual Networks for Action Segmentation in Videos | | | P. Lei and S. Todrovic., CVPR2018 |
| End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos | | | Shayamal Buch et al., BMVC 2017 |
| Cascaded Boundary Regression for Temporal Action Detection | | | Jiyang Gao et al., BMVC 2017 [ ] |
| Temporal Tessellation: A Unified Approach for Video Analysis | | | Kaufman et al., ICCV2017 |
| Temporal Action Detection with Structured Segment Networks | | | Y. Zhao et al., ICCV2017 |
| Temporal Context Network for Activity Localization in Videos | | | X. Dai et al., ICCV2017 |
| Detecting the Moment of Completion: Temporal Models for Localising Action Completion | | | F. Heidarivincheh et al., arXiv2017 |
| CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos | | | Z. Shou et al, CVPR2017 |
| SST: Single-Stream Temporal Action Proposals | | | S. Buch et al, CVPR2017 |
| R-C3D: Region Convolutional 3D Network for Temporal Activity Detection | | | H. Xu et al, arXiv2017 |
| DAPs: Deep Action Proposals for Action Understanding | | | V. Escorcia et al, ECCV2016 |
| Online Action Detection using Joint Classification-Regression Recurrent Neural Networks | | | Y. Li et al, ECCV2016. Noe: RGB-D Action Detection |
| Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs | | | Z. Shou et al, CVPR2016. Note: Aka S-CNN |
| Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos | | | F. Heilbron et al, CVPR2016. Note: Depends on , aka SparseProp |
| Actionness Estimation Using Hybrid Fully Convolutional Networks | | | L. Wang et al, CVPR2016. Note: The code is not a complete verision. It only contains a demo, not training |
| Learning Activity Progression in LSTMs for Activity Detection and Early Detection | | | S. Ma et al, CVPR2016 |
| End-to-end Learning of Action Detection from Frame Glimpses in Videos | | | S. Yeung et al, CVPR2016. Note: This method uses reinforcement learning |
| Fast Action Proposals for Human Action Detection and Search | | | 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 | | | P. Mettes et al, ICMR2015 |
| Action localization in videos through context walk | | | K. Soomro et al, ICCV2015 |
Awesome Action Recognition: / Action Recognition and Video Understanding / Spatio-Temporal Action Detection |
| A Better Baseline for AVA | | | R. Girdhar et al., ActivityNet Workshop, CVPR2018 |
| Real-Time End-to-End Action Detection with Two-Stream Networks | | | A. El-Nouby and G. Taylor, arXiv2018 |
| Human Action Localization with Sparse Spatial Supervision | | | P. Weinzaepfel et al., arXiv2017 |
| Unsupervised Action Discovery and Localization in Videos | | | K. Soomro and M. Shah, ICCV2017 |
| Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions | | | P. Mettes and C. G. M. Snoek, ICCV2017 |
| Action Tubelet Detector for Spatio-Temporal Action Localization | | | V. Kalogeiton et al, ICCV2017 |
| Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos | | | et al, ICCV2017 |
| Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection | | | M. Zolfaghari et al, ICCV2017 |
| TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal | | | H. Zhu et al., ICCV2017 |
| Online Real time Multiple Spatiotemporal Action Localisation and Prediction | | | et al, ICCV2017 |
| AMTnet: Action-Micro-Tube regression by end-to-end trainable deep architecture | | | S. Saha et al, ICCV2017 |
| Am I Done? Predicting Action Progress in Videos | | | F. Becattini et al, BMVC2017 |
| Generic Tubelet Proposals for Action Localization | | | J. He et al, arXiv2017 |
| Incremental Tube Construction for Human Action Detection | | | H. S. Behl et al, arXiv2017 |
| Multi-region two-stream R-CNN for action detection | | | and C. Schmid. ECCV2016 |
| Spot On: Action Localization from Pointly-Supervised Proposals | | | P. Mettes et al, ECCV2016 |
| Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos | | | S. Saha et al, BMVC2016 |
| Learning to track for spatio-temporal action localization | | | P. Weinzaepfel et al. ICCV2015 |
| Action detection by implicit intentional motion clustering | | | W. Chen and J. Corso, ICCV2015 |
| Finding Action Tubes | | | G. Gkioxari and J. Malik CVPR2015 |
| APT: Action localization proposals from dense trajectories | | | J. Gemert et al, BMVC2015 |
| Spatio-Temporal Object Detection Proposals | | | D. Oneata et al, ECCV2014 |
| Action localization with tubelets from motion | | | M. Jain et al, CVPR2014 |
| Spatiotemporal deformable part models for action detection | | | et al, CVPR2013 |
| Action localization in videos through context walk | | | K. Soomro et al, ICCV2015 |
| Fast Action Proposals for Human Action Detection and Search | | | G. Yu and J. Yuan, CVPR2015. Note: code for FAP is NOT available online. Note: Aka FAP |
Awesome Action Recognition: / Action Recognition and Video Understanding / Ego-Centric Action Recognition |
| Actor and Observer: Joint Modeling of First and Third-Person Videos | | | G. Sigurdsson et al., CVPR2018 |
Awesome Action Recognition: / Action Recognition and Video Understanding / Miscellaneous |
| What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment | | | P. Parma and B. T. Morris. CVPR2019 |
| PathTrack: Fast Trajectory Annotation with Path Supervision | | | S. Manen et al., ICCV2017 |
| CortexNet: a Generic Network Family for Robust Visual Temporal Representations | | | A. Canziani and E. Culurciello - arXiv2017 |
| Slicing Convolutional Neural Network for Crowd Video Understanding | | | J. Shao et al, CVPR2016 |
| Two-Stream (RGB and Flow) pretrained model weights | 26 | almost 10 years ago | |
Awesome Action Recognition: / Action Recognition and Video Understanding / Action Recognition Datasets |
| Video Dataset Overview from Antoine Miech | | | |
| HACS | | | |
| Moments in Time | | | , |
| AVA | | | , , for missing videos |
| Kinetics | | | , , |
| OOPS | | | A dataset of unintentional action, |
| COIN | | | a large-scale dataset for comprehensive instructional video analysis, |
| YouTube-8M | | | , |
| YouTube-BB | | | , |
| 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 | | | , |
| ActivityNet | | | Note: They provide a download script and evaluation code |
| Charades | | | |
| Charades-Ego | | | , - First person and third person video aligned dataset |
| EPIC-Kitchens | | | , - First person videos recorded in kitchens. Note they provide download scripts and a python library |
| Sports-1M | | | Large scale action recognition dataset |
| THUMOS14 | | | Note: It overlaps with dataset |
| THUMOS15 | | | Note: It overlaps with dataset |
| HOLLYWOOD2 | | | : |
| UCF-101 | | | , , and , and . And there are also some pre-computed spatiotemporal action detection |
| UCF-50 | | | |
| UCF-Sports | | | , note: the train/test split link in the official website is broken. Instead, you can download it from |
| HMDB | | | |
| J-HMDB | | | |
| LIRIS-HARL | | | |
| KTH | | | |
| MSR Action | | | Note: It overlaps with datset |
| Sports Videos in the Wild | | | |
| NTU RGB+D | 763 | over 4 years ago | |
| Mixamo Mocap Dataset | | | |
| UWA3D Multiview Activity II Dataset | | | |
| Northwestern-UCLA Dataset | | | |
| SYSU 3D Human-Object Interaction Dataset | | | |
| MEVA (Multiview Extended Video with Activities) Dataset | | | |
Awesome Action Recognition: / Action Recognition and Video Understanding / Video Annotation |
| Efficiently scaling up crowdsourced video annotation | | | C. Vondrick et. al, IJCV2013 |
| The Design and Implementation of ViPER | | | D. Mihalcik and D. Doermann, Technical report |
| VTT: Visual Object Tagging Tool | 4,331 | almost 5 years ago | . 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 | | | . 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 |
Awesome Action Recognition: / Object Recognition / Object Detection |
| Deformable Convolutional Networks | | | J. Dai et al., ICCV2017 |
| Detectron | 26,295 | almost 3 years ago | Open Source Object Detection Framework from Facebook AI Research. Includes Mask R-CNN, FPN, and etc. Caffe2 implementation |
| Mask R-CNN | | | K. He et al, , , , , - State-of-the-art object detection/instance segmentation algorithm |
| Faster R-CNN | | | S. Ren et al, NIPS2015. , , , - State-of-the-art object detector |
| YOLO | | | J. Redmon et al, CVPR2016. , - Fast object detector |
| YOLO9000 | | | J. Redmon and A. Farhadi, CVPR2017. - State-of-the-art object detector which can detect 9000 objects in realtime |
| SSD | | | W. Liu et al, ECCV2016. , , - State-of-the-art object detector with realtime processing speed |
| RetinaNet | | | Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár, Facebook AI Research FAIR & ICCV 2017. - State-of-the-art object detector with realtime processing speed |
Awesome Action Recognition: / Object Recognition / Video Object Detection |
| [code] | 553 | about 8 years ago | [Detect to Track and Track to Detect] - C. Feichtenhofer et al., ICCV2017. , |
| [code] | 724 | almost 5 years ago | [Flow-Guided Feature Aggregation for Video Object Detection] - X. Zhu et al., ICCV2017. , aka FGFA |
Awesome Action Recognition: / Object Recognition / Video Object Detection Datasets |
| ImageNet VID | | | |
| YouTube-8M | | | , |
| YouTube-BB | | | , |
Awesome Action Recognition: / Pose Estimation / Pose Estimation |
| AlphaPose | 8,084 | over 2 years ago | PyTorch based realtime and accurate pose estimation and tracking tool from SJTU |
| Detect-and-Track: Efficient Pose Estimation in Videos | | | R. Girdhar et al., arXiv2017 |
| OpenPose Library | 31,463 | about 2 years ago | Caffe based realtime pose estimation library from CMU |
| Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | | | Z. Cao et al, CVPR2017. depends on the - Earlier version of OpenPose from CMU |
| DensePose | | | Dense pose human estimation in the wild implemented in the Detectron framework |
| MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network | | | M. Kocabas et al, ECCV2018 |
| DeepLabCut: markerless pose estimation of user-defined body parts with deep learning | | | A. Mathis et al, Nature Neuroscience 2018 |
Awesome Action Recognition: / Competitions / Competitions |
| ActEV (Activities in Extended Video | | | Activity detection in security camera videos. Runs through 2021. Hosted by NIST |
awesome-action-recognition
jbhuang0604/awesome-computer-vision
endymecy/awesome-deeplearning-resources
0ex/more-awesomeopen-mmlab/mmdetection
terrychenism/deformable-convnets
msracver/deformable-convnets
huaizhengzhang/awsome-deep-learning-for-video-analysis
skyhehe123/sa-ssd