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Found 5 papers, 3 papers with code
FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition
To address these issues, FROSTER employs a residual feature distillation approach to ensure that CLIP retains its generalization capability while effectively adapting to the action recognition task.
Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition
Graph convolutional networks have been widely applied in skeleton-based gait recognition.
GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans.
Graph Contrastive Learning for Skeleton-based Action Recognition
In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences.
Ranked #9 on
Skeleton Based Action Recognition
on NTU RGB+D
Multi-scale Context-aware Network with Transformer for Gait Recognition
Although gait recognition has drawn increasing research attention recently, since the silhouette differences are quite subtle in spatial domain, temporal feature representation is crucial for gait recognition.
Ranked #1 on
Gait Recognition
on OUMVLP
