{"ID":2839887,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14196","arxiv_id":"2511.14196","title":"MindCross: Fast New Subject Adaptation with Limited Data for Cross-subject Video Reconstruction from Brain Signals","abstract":"Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each subject. However, the expensive cost of collecting brain-video data causes severe data scarcity. Although some cross-subject methods being introduced, they often overfocus with subject-invariant information while neglecting subject-specific information, resulting in slow fine-tune-based adaptation strategy. To achieve fast and data-efficient new subject adaptation, we propose MindCross, a novel cross-subject framework. MindCross's N specific encoders and one shared encoder are designed to extract subject-specific and subject-invariant information, respectively. Additionally, a Top-K collaboration module is adopted to enhance new subject decoding with the knowledge learned from previous subjects' encoders. Extensive experiments on fMRI/EEG-to-video benchmarks demonstrate MindCross's efficacy and efficiency of cross-subject decoding and new subject adaptation using only one model.","short_abstract":"Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each subject. However, the expensive cost of collecting brain-video data causes severe data scarcity. Althou...","url_abs":"https://arxiv.org/abs/2511.14196","url_pdf":"https://arxiv.org/pdf/2511.14196v1","authors":"[\"Xuan-Hao Liu\",\"Yan-Kai Liu\",\"Tianyi Zhou\",\"Bao-Liang Lu\",\"Wei-Long Zheng\"]","published":"2025-11-18T07:04:36Z","proceeding":"cs.MM","tasks":"[\"cs.MM\",\"cs.CV\",\"cs.HC\"]","methods":"[]","has_code":false}