Dabin Sheng — accepted AI-conference papers

2026

BrainLMM: A Label-Free Framework for Mapping Multi-Semantic Representation in the Human Visual Cortex

Tan Gao, Mufan Xue, Haofang Zheng, Shuo Lv, Jia Xu, Dabin Sheng +4

AAAI 2026technical

Previous studies leveraging artificial neural networks have been used to investigate the semantic coding within human visual cortex. However, building an interpretable label-free framework that can effectively map brain responses to multiple coexisting semantic concepts remains largely unexplored. H

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2025

CLIP-MSM: A Multi-Semantic Mapping Brain Representation for Human High-Level Visual Cortex

Guoyuan Yang, Mufan Xue, Ziming Mao, Haofang Zheng, Jia Xu, Dabin Sheng +3

AAAI 2025technical

Prior work employing deep neural networks (DNNs) with explainable techniques has identified human visual cortical selective representation to specific categories. However, constructing high-performing encoding models that accurately capture brain responses to coexisting multi-semantics remains elusi…

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