The video classification task has gained significant success in the recent years. Specifically,
the topic has gained more attention after the emergence of deep learning models as a …

Current few-shot action recognition methods reach impressive performance by learning
discriminative features for each video via episodic training and designing various temporal …

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …

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

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

While the majority of FSL models focus on image classification, the extension to action
recognition is rather challenging due to the additional temporal dimension in videos. To …

Few-shot action recognition aims to recognize actions in test videos based on limited
annotated data of target action classes. The dominant approaches project videos into a …

A recent study [4] finds that existing few-shot learning methods, trained on the source
domain, fail to generalize to the novel target domain when a domain gap is observed. This …

Due to the scarcity of manually annotated data required for fine-grained video
understanding, few-shot fine-grained (FS-FG) action recognition has gained significant …

Few-shot action recognition aims to recognize novel action classes using only a small
number of labeled training samples. In this work, we propose a novel approach that first …