In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …

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

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 …

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

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 …

Learning from large-scale contrastive language-image pre-training like CLIP has shown
remarkable success in a wide range of downstream tasks recently, but it is still under …

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 …

Recent few-shot video classification (FSVC) works achieve promising performance by
capturing similarity across support and query samples with different temporal alignment …

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 …

We present a novel method for few-shot video classification, which performs appearance
and temporal alignments. In particular, given a pair of query and support videos, we conduct …