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20222026
most citedVisual Adversarial Attacks and Defenses in the Physical World: A Survey

5 citations · 12 across the 21 of their papers we have counts for

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cs.CV2026

VQ-Jarvis: Retrieval-Augmented Video Restoration Agent with Sharp Vision and Fast Thought

Xuanyu Zhang, Weiqi Li, Qunliang Xing +6

Video restoration in real-world scenarios is challenged by heterogeneous degradations, where static architectures and fixed inference pipelines often fail to generalize. Recent age…

cs.CV2025

VRSA: Jailbreaking Multimodal Large Language Models through Visual Reasoning Sequential Attack

Shiji Zhao, Shukun Xiong, Yao Huang +7

Multimodal Large Language Models (MLLMs) are widely used in various fields due to their powerful cross-modal comprehension and generation capabilities. However, more modalities bri…

cs.CV2025

Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective

Maoxun Yuan, Duanni Meng, Ziteng Xi +4

Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets…

cs.CV2025

Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation

Shiji Zhao, Chi Chen, Ranjie Duan +2

Adversarial Training (AT) is widely recognized as an effective approach to enhance the adversarial robustness of Deep Neural Networks. As a variant of AT, Adversarial Robustness Di…

cs.CV2025

MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models

Shiji Zhao, Qihui Zhu, Shukun Xiong +7

Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…

cs.CV2025

The Path to Reconciling Quality and Safety in Text-to-Image Generation: Dataset, Method, and Evaluation

Shouwei Ruan, Zhenyu Wu, Yao Huang +5

Content safety is a fundamental challenge for text-to-image (T2I) models, yet prevailing methods enforce a debilitating trade-off between safety and generation quality. We argue th…