3 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance
Shengyang Sun, Jiashen Hua, Junyi Feng +1
Weakly supervised multimodal video anomaly detection has gained significant attention, yet the potential of the text modality remains under-explored. Text provides explicit semanti…
Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection
Shengyang Sun, Xiaojin Gong
Weakly supervised multimodal violence detection aims to learn a violence detection model by leveraging multiple modalities such as RGB, optical flow, and audio, while only video-le…
Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detection
Shengyang Sun, Xiaojin Gong
Weakly supervised video anomaly detection (WS-VAD) is a challenging problem that aims to learn VAD models only with video-level annotations. In this work, we propose a Long-Short T…
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection
Shengyang Sun, Xiaojin Gong
Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware VAD mo…