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 …

The task of few-shot action recognition aims to recognize novel action classes using only a
small number of labeled training samples. How to better describe the action in each video …

Few-shot action recognition seeks to classify new action categories using only a few labeled
video samples as reference. Due to the lack of sufficient training samples and the complex …

For few-shot video action recognition, it is essential to extract and align features from
different videos. However, these operations can be complicated and unreliable due to the …

Adapting large pre-trained image models to few-shot action recognition has proven to be an
effective and efficient strategy for learning robust feature extractors, which is essential for few …

Human action video recognition has recently attracted more attention in applications such as
video security and sports posture correction. Popular solutions, including graph …

This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th
Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in …

Despite the progress made in few-shot video action recognition, existing methods still
struggle to achieve satisfactory performance when support samples are limited (eg, 1-shot …

Few-shot action recognition aims to accurately predict unseen action categories from limited
data. To capture the complex temporal variations within videos, we focus on spatio-temporal …

Meta-learning in the broader context concerns how an agent learns about their own
learning, allowing them to improve their learning process. Learning how to learn is not only …