Overview

• Number of checkpoints: 179

• Number of configs: 177

• Number of papers: 33

  • ALGORITHM: 28

  • BACKBONE: 1

  • DATASET: 3

  • OTHERS: 1

For supported datasets, see datasets overview.

Spatio Temporal Action Detection Models

• Number of checkpoints: 25

• Number of configs: 27

• Number of papers: 5

  • [ALGORITHM] Ava: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions (-> -> ->)

  • [ALGORITHM] Slowfast Networks for Video Recognition (->)

  • [ALGORITHM] The Ava-Kinetics Localized Human Actions Video Dataset (->)

  • [DATASET] Ava: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions (-> -> ->)

  • [DATASET] The Ava-Kinetics Localized Human Actions Video Dataset (->)

Action Localization Models

• Number of checkpoints: 2

• Number of configs: 2

• Number of papers: 3

  • [ALGORITHM] Bmn: Boundary-Matching Network for Temporal Action Proposal Generation (->)

  • [ALGORITHM] Bsn: Boundary Sensitive Network for Temporal Action Proposal Generation (->)

  • [DATASET] Cuhk & Ethz & Siat Submission to Activitynet Challenge 2017 (->)

Action Recognition Models

• Number of checkpoints: 114

• Number of configs: 111

• Number of papers: 22

  • [ALGORITHM] A Closer Look at Spatiotemporal Convolutions for Action Recognition (->)

  • [ALGORITHM] Audiovisual Slowfast Networks for Video Recognition (->)

  • [ALGORITHM] Is Space-Time Attention All You Need for Video Understanding? (->)

  • [ALGORITHM] Learning Spatiotemporal Features With 3d Convolutional Networks (->)

  • [ALGORITHM] Mvitv2: Improved Multiscale Vision Transformers for Classification and Detection (->)

  • [ALGORITHM] Non-Local Neural Networks (-> -> ->)

  • [ALGORITHM] Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset (->)

  • [ALGORITHM] Slowfast Networks for Video Recognition (-> -> ->)

  • [ALGORITHM] Tam: Temporal Adaptive Module for Video Recognition (->)

  • [ALGORITHM] Temporal Interlacing Network (->)

  • [ALGORITHM] Temporal Pyramid Network for Action Recognition (->)

  • [ALGORITHM] Temporal Relational Reasoning in Videos (->)

  • [ALGORITHM] Temporal Segment Networks: Towards Good Practices for Deep Action Recognition (->)

  • [ALGORITHM] Tsm: Temporal Shift Module for Efficient Video Understanding (->)

  • [ALGORITHM] Uniformer: Unified Transformer for Efficient Spatial-Temporal Representation Learning (->)

  • [ALGORITHM] Uniformerv2: Spatiotemporal Learning by Arming Image Vits With Video Uniformer (->)

  • [ALGORITHM] Video Classification With Channel-Separated Convolutional Networks (->)

  • [ALGORITHM] Video Swin Transformer (->)

  • [ALGORITHM] Video{mae (->)

  • [ALGORITHM] X3d: Expanding Architectures for Efficient Video Recognition (->)

  • [BACKBONE] Non-Local Neural Networks (-> -> ->)

  • [OTHERS] Large-Scale Weakly-Supervised Pre-Training for Video Action Recognition (->)

Skeleton-based Action Recognition Models

• Number of checkpoints: 38

• Number of configs: 37

• Number of papers: 4

  • [ALGORITHM] Pyskl: Towards Good Practices for Skeleton Action Recognition (->)

  • [ALGORITHM] Revisiting Skeleton-Based Action Recognition (->)

  • [ALGORITHM] Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (->)

  • [ALGORITHM] Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition (->)