4 papers · 1 filter
Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Models
Baoheng Zhang, Jiahui Liu, Gui Zhao +7
Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable stru…
Aligning Effective Tokens with Video Anomaly in Large Language Models
Yingxian Chen, Jiahui Liu, Ruidi Fan +6
Understanding abnormal events in videos is a vital and challenging task that has garnered significant attention in a wide range of applications. Although current video understandin…
Can OOD Object Detectors Learn from Foundation Models?
Jiahui Liu, Xin Wen, Shizhen Zhao +2
Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such a…
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Yingxian Chen, Zhengzhe Liu, Baoheng Zhang +3
Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long vi…