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/gordicaleksa/pytorch-GAT
https://github.com/gouthamvgk/coreml_conversion_hub
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gpspelle/Convert_channel
https://github.com/gregorbachmann/scaling_mlps
https://github.com/gsig/charades-algorithms
https://github.com/guide2157/ChulaXrayClassifier
What Makes Training Multi-Modal Classification Networks Hard?
https://github.com/guiggh/hand_pose_action
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
https://github.com/guilk/diva
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/gulvarol/ltc
https://github.com/gulvarol/surreact
Synthetic Humans for Action Recognition from Unseen Viewpoints
https://github.com/guoxih/busy-quiet-net
https://github.com/haamoon/mmtm
https://github.com/halimasoulb/human-pose-estimation-3d-lidar
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/handasontam/GAT-with-edgewise-attention
https://github.com/handhand123/prsa-net
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/hangzhaomit/HACS-dataset
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
https://github.com/haofanwang/video-swin-transformer-pytorch
https://github.com/haomo-ai/superfusion
https://github.com/happen2me/cross-gnn
https://github.com/happygds/two_level
https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hayatkhan8660-maker/DVFL-Net
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/hazdzz/ChebyNet
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/hazdzz/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/hazdzz/SGC
https://github.com/hehefan/P4Transformer
https://github.com/hehefan/PSTNet2
https://github.com/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hhe-distance/AIF-CNN
https://github.com/hi-archers/stableke
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/highwaywu/imageforensicsosn
https://github.com/hildekuehne/Weak_YouTube_dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.