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SotA/Methodology/Zero-Shot Learning/N-UCLA

Zero-Shot Learning on N-UCLA

Metric: Accuracy (higher is better)

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#Model↕Accuracy▼AugmentationsPaperDate↕Code
1DSCNet (RGB + Pose)99.1No--Code
2SkateFormer98.3NoSkateFormer: Skeletal-Temporal Transformer for H...2024-03-14Code
3LA-GCN97.6NoLanguage Knowledge-Assisted Representation Learn...2023-05-21Code
4MMCL97.5YesMulti-Modality Co-Learning for Efficient Skeleto...2024-07-22Code
5TD-GCN97.4No--Code
6Action Capsules97.3NoAction Capsules: Human Skeleton Action Recognition2023-01-30-
7LST97.2NoGenerative Action Description Prompts for Skelet...2022-08-10Code
8HD-GCN97.2NoHierarchically Decomposed Graph Convolutional Ne...2022-08-23Code
9InfoGCN97No--Code
10TCA-GCN97NoSkeleton-based Action Recognition via Temporal-C...2022-05-31Code
11DSTSA-GCN96.98NoDSTSA-GCN: Advancing Skeleton-Based Gesture Reco...2025-01-21Code
12JPFormer96.7No--Code
13CTR-GCN96.5NoChannel-wise Topology Refinement Graph Convoluti...2021-07-26Code
14TD-GDSCN95.69No--Code
15MSSTNet95.3No--Code
16Hierarchical Action Classification (RGB + Pose)93.99NoHierarchical Action Classification with Network ...2020-07-30-
17MMNet (RGB + Pose)93.7Yes--Code
18VPN (RGB + Pose)93.5YesVPN: Learning Video-Pose Embedding for Activitie...2020-07-06Code
19VPN++ (RGB + Pose)93.5YesVPN++: Rethinking Video-Pose embeddings for unde...2021-05-17Code