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Action Recognition

Activity Recognition › Action Recognition 1,538 평가 기록 172 datasets

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.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
Hulk(Finetune, ViT-L)
Accuracy (CS): 94.3
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MVD (Kinetics400 pretrain, ViT-H, 16 frame)
Top-1 Accuracy: 77.3Top-5 Accuracy: 95.7Parameters: 633GFLOPs: 1192x6
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FTP-UniFormerV2-L/14
3-fold Accuracy: 99.7
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ProtoGCN
Accuracy (Cross-Subject): 90.9Accuracy (Cross-Setup): 92.2Ensembled Modalities: 6
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VideoMAE V2-g
Average accuracy of 3 splits: 88.7
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InternVideo
Top 1 Accuracy: 70.0
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VideoMAE V2-g
3-fold Accuracy: 99.6
논문 코드
MVD (ViT-B)
Top-1 Accuracy: 79.7Pre-Training Dataset: Kinetics400Frozen: false
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Structured Keypoint Pooling (PPNv2 skeletons+objects)
Accuracy: 52.3
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LART (Hiera-H, K700 PT+FT)
mAP: 45.1
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LLaVAction
Action@1: 58.3Verb@1: 76Noun@1: 69
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DSCNet (RGB + Pose)
Accuracy (CS): 97.4Accuracy (CV): 99.4
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