Current state-of-the-art approaches for few-shot action recognition achieve promising
performance by conducting frame-level matching on learned visual features. However, they …

Class prototype construction and matching are core aspects of few-shot action recognition.
Previous methods mainly focus on designing spatiotemporal relation modeling modules or …

Learning from large-scale contrastive language-image pre-training like CLIP has shown
remarkable success in a wide range of downstream tasks recently, but it is still under …

Few-shot action recognition aims to address the high cost and impracticality of manually
labeling complex and variable video data in action recognition. It requires accurately …

Applying large-scale pre-trained visual models like CLIP to few-shot action recognition tasks
can benefit performance and efficiency. Utilizing the" pre-training, fine-tuning" paradigm …

Few-shot action recognition is a challenging but practical problem aiming to learn a model
that can be easily adapted to identify new action categories with only a few labeled samples …

In this paper we tackle the task of category-agnostic pose estimation (CAPE) which aims to
predict poses for objects of any category with few annotated samples. Previous works either …

Existing few-shot action recognition methods have placed primary focus on improving the
recognition accuracy while neglecting another important indicator in practical scenarios, ie …

Existing works in few-shot action recognition mostly fine-tune a pre-trained image model and
design sophisticated temporal alignment modules at feature level. However, simply fully fine …

Recent work on action recognition leverages 3D features and textual information to achieve
state-of-the-art performance. However, most of the current few-shot action recognition …