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

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 …

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

Recently, few-shot action recognition has progressed significantly, as it has learned the
feature discriminability and designed suitable comparison methods. Still, there are the …

Stroke is the prevalent cerebrovascular disease characterized by significant incidence and
disability rates. To enhance the early perceive and detection of potential stroke patients, the …

Few-shot action recognition is an emerging field in computer vision, primarily focused on
meta-learning within the same domain. However, challenges arise in real-world scenario …

In the research field of few-shot learning, the main difference between image-based and
video-based is the additional temporal dimension. In recent years, some works have used …

The metric learning paradigm has achieved notable success in few-shot action recognition;
however, it faces unaddressed challenges. Specifically,(1) limited training data could …

One-shot action recognition, which refers to recognizing human-performed actions using
only a single training example, holds significant promise in advancing video analysis …