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 …

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 …

There are over 150 sign languages worldwide, each with numerous local variants and
thousands of signs. However, collecting annotated data for each sign language to train a …

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 …

Human action recognition (HAR) plays a key role in various applications such as video
analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms …

Few-shot video action recognition (FS-VAR) is a challenging task that requires models to
have significant expressive power in order to identify previously unseen classes using only a …

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 …

Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has
attracted recent research interests by solving the domain gap lying in source-to-target …

Recognizing novel actions based on few labeled skeleton sequence samples is a promising
field. Existing works primarily focus on global representations of similar actions and …

Few-shot action recognition aims to train a model to classify actions in videos using only a
few examples, known as" shots," per action class. This learning approach is particularly …