Abstract: This paper presents Action-SGFA, a novel action feature alignment approach to learn unified joint embeddings across four action modalities incorporating scene graph (SG) comprehension. A new ...
Vision-Language Adaptive Clustering and Meta-Adaptation for Unsupervised Few-Shot Action Recognition
Abstract: Unsupervised few-shot action recognition is a practical but challenging task, which adapts knowledge learned from unlabeled videos to novel action classes with only limited labeled data.
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