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cs.CV2026
Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain
Daizong Liu, Junhao Dong, Zhiyuan Ma +6
Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information, showing remarkable progress…
cs.CV2026
Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
Xiang Fang, Zeyu Xiong, Wanlong Fang +7
This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that uti…
cs.CV2026
TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
Qiucheng Yu, Ruijie Xu, Mingang Chen +2
Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental…