Large vision-language models (LVLMs) such as Qwen-VL series have demonstrated strong capabilities in visual understanding and reasoning, particularly for static images. However, extending them to video reasoning remains computationally expensive, often incurring substantial latency and token usage under conventional Ch...
Zhao-Yang Wei, Wen-Chao Ding, Chen-Hui Qiang et al.· Proceedings of the 34th ACM...· 0 citations
Large vision-language models (LVLMs) such as Qwen-VL series have demonstrated strong capabilities in visual understanding and reasoning, particularly for static images. However, extending them to video reasoning remains computationally expensive, often incurring substantial latency and token usage under conventional Ch...
Zhao-Yang Wei, Wen-Chao Ding, Chen-Hui Qiang et al.· 0 citations
Backdoor as Probe is proposed, a test-time adversarial defense for CLIP that improves average robust accuracy from 1.0\% to 52.3\% while retaining clean accuracy, achieving performance comparable to state-of-the-art methods with up to a \(5.7\times\) inference speedup.
Zhong Ling Wang, Jie Zhang, Sen Nie et al.· 0 citations
This work proposes O-Attack, a highly transferable black-box attack framework that outperforms six state-of-the-art methods in black-box transferability, with consistent effectiveness across prompts and improved efficiency and imperceptibility.
Sen Nie, Jie Zhang, Zhong Ling Wang et al.· arXiv.org· 0 citations
Video-HolmesV2 is presented, a novel benchmark designed for Deep Audio-Visual Coupling that enforces an Evidence-Based Evaluation, requiring models to justify answers with precise spatio-temporal audio-visual evidence, thereby reducing confounding effects of guessing and hallucinated evidence.
Zhao-Yang Wei, Zi-Peng Wang, Yu-She Cao et al.· 0 citations
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