Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

This paper proposes a novel weakly supervised approach for anomaly detection, which
begins with a relation-aware feature extractor to capture the multi-scale convolutional neural …

Self-supervised foundation models have shown great potential in computer vision thanks to
the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing the …

With continued improvements in wireless sensing technology, the notion of the Internet of
Things (IoT) has been widely adopted and has become pervasive owing to its broad …

Efficient action recognition has become crucial to extend the success of action recognition to
many real-world applications. Contrary to most existing methods, which mainly focus on …

With the surge in attention to Egocentric Hand-Object Interaction (Ego-HOI), large-scale
datasets such as Ego4D and EPIC-KITCHENS have been proposed. However, most current …

This paper presents a new relational network for group activity recognition. The essence of
the network is to integrate conditional random fields (CRFs) with self-attention to infer the …

Recent methods including CoViAR and DMC-Net provide a new paradigm for action
recognition since they are directly targeted at compressed videos (eg, MPEG4 files). It …

A large proportion of construction accidents are caused by workers' unsafe actions. Due to
the complexity of the work environment and excessive demands of safety supervision on …

Abstract Human Activity Recognition (HAR) and fall detection, as applications within the field
of biomedical signal processing, are increasingly pivotal in enhancing patient care …