awesome-action-recognition
by jinwchoi
A curated list of action recognition and related area resources
AI summary
Action Recognition Resources
A curated collection of resources and research papers on action recognition and video understanding techniques.
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183 links in 15 sections, with live GitHub stats.activeno commit in 2y
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
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
- 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
[Decord]
Efficient video reader for python
[3D ResNet PyTorch]
[PyTorch Video Research]
[M-PACT: Michigan Platform for Activity Classification in Tensorflow]
[Inflated models on PyTorch]
[A Two Stream Baseline on Kinectics dataset]
[I3D models converted from Tensorflow to Core ML]
[Extract frame and optical-flow from videos, #docker]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
Action Recognition and Video Understanding / Skeleton-Based Action Classification
- Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
M. Li 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
- Part-based Graph Convolutional Network for Action Recognition
K. Thakkar et al., BMVC2018
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
- 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
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
- 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
- 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
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
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 weightsAction Recognition and Video Understanding / Action Recognition Datasets
- AVA
, , for missing videos
- Kinetics
, ,
- OOPS
A dataset of unintentional action,
- COIN
a large-scale dataset for comprehensive instructional video analysis,
- 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
- ActivityNet
Note: They provide a download script and evaluation code
- 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
- UCF-101
, , and , and . And there are also some pre-computed spatiotemporal action detection
- UCF-Sports
, note: the train/test split link in the official website is broken. Instead, you can download it from
- MSR Action
Note: It overlaps with datset
NTU RGB+DAction 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
. 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
Object Recognition / Object Detection
- Deformable Convolutional Networks
J. Dai et al., ICCV2017
Detectron
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
Object Recognition / Video Object Detection
[code]Object Recognition / Video Object Detection Datasets
Pose Estimation / Pose Estimation
AlphaPose
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
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
Competitions / Competitions
- ActEV (Activities in Extended Video
Activity detection in security camera videos. Runs through 2021. Hosted by NIST
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