Zhenchen Tang — accepted AI-conference papers

2026

Revisiting MLLM Based Image Quality Assessment: Errors and Remedy

Zhenchen Tang, Songlin Yang, Bo Peng, Zichuan Wang, Jing Dong

AAAI 2026technical

The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks. This discr

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2026

Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination

Zichuan Wang, Songlin Yang, Bo Peng, Zhenchen Tang, Yang Li, Beibei Dong +1

CVPR 2026

Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient visual attention. However, in this work, we are surprised to find that both real and hallucinated objects receive equally

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