3 citations · 7 across the 8 of their papers we have counts for
9 papers
AFTER: Mitigating the Object Hallucination of LVLM via Adaptive Factual-Guided Activation Editing
Tianbo Wang, Yuqing Ma, Kewei Liao +4
Large Vision-Language Models (LVLMs) have achieved substantial progress in cross-modal tasks. However, due to language bias, LVLMs are susceptible to object hallucination, which ca…
Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Hanxiao Li +13
In cooperative Multi-Agent Reinforcement Learning (MARL), it is a common practice to tune hyperparameters in ideal simulated environments to maximize cooperative performance. Howev…
Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Zheng Yuwei +12
Partial agent failure becomes inevitable when systems scale up, making it crucial to identify the subset of agents whose failure causes worst-case system performance degradations.…
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Simin Li, Ruixiao Xu, Jingqiao Xiu +4
In multi-agent reinforcement learning (MARL), ensuring robustness against unpredictable or worst-case actions by allies is crucial for real-world deployment. Existing robust MARL m…
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Simin Li, Shuing Zhang, Gujun Chen +6
Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real…
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
Simin Li, Jun Guo, Jingqiao Xiu +6
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case acti…
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu +8
This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performanc…
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection
Simin Li, Huangxinxin Xu, Jiakai Wang +4
Billions of people are sharing their daily life images on social media every day. However, their biometric information (e.g., fingerprint) could be easily stolen from these images.…
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning
Jun Guo, Yonghong Chen, Yihang Hao +3
While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversar…