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cs.CV2025
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models
Rui Hu, Delai Qiu, Shuyu Wei +4
Omnimodal Large Language Models (OLLMs) have shown significant progress in integrating vision and text, but still struggle with integrating vision and audio, often exhibiting subop…
cs.CV2025
CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs
Jiaming Zhang, Rui Hu, Qing Guo +1
Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial atta…
cs.CV2025
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
Rui Hu, Yahan Tu, Shuyu Wei +2
Despite achieving outstanding performance on various cross-modal tasks, current large vision-language models (LVLMs) still suffer from hallucination issues, manifesting as inconsis…