2 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
IGME: Efficient Chained Method Ensemble for Transferable Semantic Segmentation Attacks
Mengqi He, Jing Zhang
Semantic segmentation models are vulnerable to transferable adversarial perturbations, yet evaluating transfer attacks on dense prediction models can be computationally expensive.…
Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization
Mengqi He, Xinyu Tian, Xin Shen +6
Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability, casting doubt on the feasibili…
All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Models
Xinyu Tian, Shu Zou, Zhaoyuan Yang +3
Recent studies have demonstrated that Reinforcement Learning (RL), notably Group Relative Policy Optimization (GRPO), can intrinsically elicit and enhance the reasoning capabilitie…
High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models
Mengqi He, Xinyu Tian, Xin Shen +4
Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, as a measure of model uncertainty, is highly correlated with VLM…
More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
Xinyu Tian, Shu Zou, Zhaoyuan Yang +5
Reasoning has emerged as a pivotal capability in Large Language Models (LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy Optimization (GRPO), these model…
Transferable Attack for Semantic Segmentation
Mengqi He, Jing Zhang, Zhaoyuan Yang +3
We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target…
Salient Object Detection via Bounding-box Supervision
Mengqi He, Jing Zhang, Wenxin Yu
The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency…