Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

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 …

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 …

In recent years, the field of few-shot action recognition (FSAR) has garnered significant
attention. Although many methods primarily rely on mono-modal data, there is a growing …

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 classification (FSC) is a challenging problem, which aims to identify novel classes
with limited samples. Most existing methods employ vanilla transfer learning or episodic …

In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot
action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre …

Current methods for few-shot action recognition mainly fall into the metric learning
framework following ProtoNet, which demonstrates the importance of prototypes. Although …

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 few-shot action recognition (FSAR) methods typically perform semantic matching on
learned discriminative features to achieve promising performance. However, most FSAR …