MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction

• URL: http://arxiv.org/abs/2404.12630v1
• Date: Fri, 19 Apr 2024 05:12:04 GMT
• Title: MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
• Authors: Zixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu, Ke Liu, Liang Hu, Duoqian Miao,
• Abstract summary: Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects. MindTuner achieves high-quality and rich-semantic reconstructions using only 1 hour of fMRI training data.
• Score: 21.531569319105877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and the scarcity of data annotation. In this work, we proposed MindTuner for cross-subject visual decoding, which achieves high-quality and rich-semantic reconstructions using only 1 hour of fMRI training data benefiting from the phenomena of visual fingerprint in the human visual system and a novel fMRI-to-text alignment paradigm. Firstly, we pre-train a multi-subject model among 7 subjects and fine-tune it with scarce data on new subjects, where LoRAs with Skip-LoRAs are utilized to learn the visual fingerprint. Then, we take the image modality as the intermediate pivot modality to achieve fMRI-to-text alignment, which achieves impressive fMRI-to-text retrieval performance and corrects fMRI-to-image reconstruction with fine-tuned semantics. The results of both qualitative and quantitative analyses demonstrate that MindTuner surpasses state-of-the-art cross-subject visual decoding models on the Natural Scenes Dataset (NSD), whether using training data of 1 hour or 40 hours.

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