Driver distraction has become a significant cause of severe traffic accidents over the past
decade. Despite the growing development of vision-driven driver monitoring systems, the …

Abstract Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that
aims to perceive the emotional states of the target person with contextual information …

Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

Weakly supervised video anomaly detection aims to locate abnormal activities in untrimmed
videos without the need for frame-level supervision. Prior work has utilized graph …

Learning a policy with great generalization to unseen environments remains challenging but
critical in visual reinforcement learning. Despite the success of augmentation combination in …

Context-aware emotion recognition (CAER) has recently boosted the practical applications
of affective computing techniques in unconstrained environments. Mainstream CAER …

Multi-agent collaborative perception has received increasing attention recently as an
emerging application in driving scenarios. Despite advancements in previous approaches …

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the
normality in videos. As a long-standing task in the field of computer vision, VAD has …

Abstract Multimodal Sentiment Analysis (MSA) aims to understand human intentions by
integrating emotion-related clues from diverse modalities, such as visual, language, and …

Abstract Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …