1 citations · 1 across the 4 of their papers we have counts for
7 papers
MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial Purification
Xiaoyi Huang, Junwei Wu, Kejia Zhang +2
Adversarial purification with diffusion models has emerged as a promising defense strategy, but existing methods typically rely on uniform noise injection, which indiscriminately p…
TARS: MinMax Token-Adaptive Preference Strategy for Hallucination Reduction in MLLMs
Kejia Zhang, Keda Tao, Zhiming Luo +3
Multimodal large language models (MLLMs) are prone to hallucinations, generating plausible but visually ungrounded outputs, partly because direct preference optimization (DPO) over…
A Survey of Token Compression for Efficient Multimodal Large Language Models
Kele Shao, Keda Tao, Kejia Zhang +7
Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution im…
Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs
Kejia Zhang, Keda Tao, Jiasheng Tang +1
Large vision-language models (LVMs) extend large language models (LLMs) with visual perception capabilities, enabling them to process and interpret visual information. A major chal…
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment
Kejia Zhang, Juanjuan Weng, Shaozi Li +1
Despite the remarkable progress of deep neural networks (DNNs) in various visual tasks, their vulnerability to adversarial examples raises significant security concerns. Recent adv…
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness
Kejia Zhang, Juanjuan Weng, Yuanzheng Cai +2
Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision. While adversarial training (AT) has emerged as a pro…
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness
Kejia Zhang, Juanjuan Weng, Junwei Wu +3
The vulnerability of Deep Neural Networks to adversarial perturbations presents significant security concerns, as the imperceptible perturbations can contaminate the feature space…