Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zphyix/tcm
공식 코드Motion-driven Visual Tempo Learning for Video-based Action Recognition
https://github.com/zsofiak/masterthesis
공식 코드MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal Videos
https://github.com/zwx8981/BVQA-2021
공식 코드Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
https://github.com/zzhao71/ripplecot
공식 코드https://github.com/zzysteve/skeletonx
공식 코드SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
https://gitlab.com/MISLgit/forensic-similarity-for-digital-images
공식 코드https://gitlab.com/nct_tso_public/surgvgen
공식 코드Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models
https://gitlab.engr.illinois.edu/hubris/mesrnn
공식 코드Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
https://huggingface.co/datasets/SoftMINER-Group/NicheHazardQA
공식 코드Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
https://huggingface.co/datasets/SoftMINER-Group/TechHazardQA
공식 코드https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://gitee.com/zhengkeli/zkl-knowedit-rome
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/1zgh/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2023-MindSpore-1/ms-code-187
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stgcn
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/2023-MindSpore-1/ms-code-35
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-68
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsn
https://github.com/2023-MindSpore-4/Code-5/tree/main/MVD
https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code10/tree/main/glore_res
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.