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 …

Recently, few-shot action recognition receives increasing attention and achieves remarkable
progress. However, previous methods mainly rely on limited unimodal data (eg, RGB …

Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods
directly employ optical flow or deformable convolution to predict full-spectrum motion fields …

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 …

Learning discriminative features from very few labeled samples to identify novel classes has
received increasing attention in skeleton-based action recognition. Existing works aim to …

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 has attracted increasing attention in recent years, but it remains
challenging due to the intrinsic difficulty in learning transferable knowledge to generalize to …

Current few-shot action recognition approaches have achieved impressive performance
using only a few labeled examples. However, they usually assume the base (train) and …