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Activity Recognition

1,654 평가 기록 208 datasets

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

<span class="description-source">Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters </span>

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
논문 코드
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
논문 코드

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