Author

Zenglin Shi

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Jul 2026

Visual Token Compression Enhances Robustness of MLLMs

This paper shows for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbreak attacks and hallucinations, and maintains strong results on general datasets like MME.

Shishen Gu, Jie-Quan Cui, Wen-Bo Hu et al. · arXiv.org · 2 citations

Jul 2026

Boosting Semi-Supervised Learning With Entropy-Guided Adaptive Reward Maximization

Existing semi-supervised learning (SSL) methods rely predominantly on pseudo-labeling and consistency regularization to leverage unlabeled data, demonstrating significant performance improvements. However, we pinpoint that these methods suffer from a confidence-for-weighting issue, overvaluing high-confidence pseudo-la...

Anyang Tong, Zenglin Shi, Zhun Zhong et al. · IEEE Transactions on Image P... · 0 citations