Unified Keypoint-Based Action Recognition Framework via Structured Keypoint Pooling
Ryo Hachiuma, Fumiaki Sato, Taiki Sekii
2023年份
6顶会引用
摘要
Skeleton-based Spatio-temporal Action Localization Skeleton-based Action Recognition 𝑡 𝑡 𝑡 Figure 1. Qualitative results of the proposed framework for the skeleton-based action recognition (top) and spatio-temporal localization task (bottom). The input keypoints and the estimated action labels are visualized in the figure. We achieve state-of-the-art accuracy for the recognition task while it runs ∼1800FPS on a single RTX 3080Ti GPU. In addition, the proposed method outperforms the state-of-the-art weakly supervised spatio-temporal localization methods. See the website for the demo video.
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引用它的顶会 Paper6
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- InsAT: Instance-aware Semantic Alignment and Transfer from Human-Object Keypoints for Zero-to-Few-shot Action UnderstandingKazuki TsutsukawaACL 2026
它引用的顶会 Paper14
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- InfoGCN: Representation Learning for Human Skeleton-based Action RecognitionHyung-Gun Chi, Myoung Hoon Ha, Seung-geun Chi, Sang Wan Lee 等CVPR 2022 · 被引用 383 次
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