Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception
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 …
decade. Despite the growing development of vision-driven driver monitoring systems, the …
Context de-confounded emotion recognition
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 …
aims to perceive the emotional states of the target person with contextual information …
Vision-based traffic accident detection and anticipation: A survey
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 …
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …
Improving generalization in visual reinforcement learning via conflict-aware gradient agreement augmentation
Learning a policy with great generalization to unseen environments remains challenging but
critical in visual reinforcement learning. Despite the success of augmentation combination in …
critical in visual reinforcement learning. Despite the success of augmentation combination in …
Robust emotion recognition in context debiasing
Context-aware emotion recognition (CAER) has recently boosted the practical applications
of affective computing techniques in unconstrained environments. Mainstream CAER …
of affective computing techniques in unconstrained environments. Mainstream CAER …
What2comm: Towards communication-efficient collaborative perception via feature decoupling
Multi-agent collaborative perception has received increasing attention recently as an
emerging application in driving scenarios. Despite advancements in previous approaches …
emerging application in driving scenarios. Despite advancements in previous approaches …
Stochastic video normality network for abnormal event detection in surveillance videos
Abstract Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
Learning causality-inspired representation consistency for video anomaly detection
Video anomaly detection is an essential yet challenging task in the multimedia community,
with promising applications in smart cities and secure communities. Existing methods …
with promising applications in smart cities and secure communities. Existing methods …
Sampling to distill: Knowledge transfer from open-world data
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance
student models using only the teacher network without original training data. Despite …
student models using only the teacher network without original training data. Despite …
Osin: Object-centric scene inference network for unsupervised video anomaly detection
Video Anomaly Detection (VAD) is an essential yet challenging task in the signal processing
community, which aims to understand the spatial and temporal contextual interactions …
community, which aims to understand the spatial and temporal contextual interactions …