Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailored to each subject. The personalization limits the broader applicability of brain visual decoding in real-world scenarios. To address this issue, we introduce Wills Aligner, a novel approach designed to achieve multi-subject collaborative brain visual decoding. Wills Aligner begins by aligning the fMRI data from different subjects at the anatomical level. It then employs delicate mixture-of-brain-expert adapters and a meta-learning strategy to account for individual fMRI pattern differences. Additionally, Wills Aligner leverages the semantic relation of visual stimuli to guide the learning of inter-subject commonality, enabling visual decoding for each subject to draw insights from other subjects' data. We rigorously evaluate our Wills Aligner across various visual decoding tasks, including classification, cross-modal retrieval, and image reconstruction. The experimental results demonstrate that Wills Aligner achieves promising performance.

翻译：从人类大脑活动中解码视觉信息在近年研究中取得了显著进展。然而，个体间皮层分区与功能磁共振成像模式的差异性，促使了针对单一被试的深度学习模型的发展。这种个性化处理限制了脑视觉解码在现实场景中的广泛适用性。为解决该问题，我们提出了Wills Aligner，一种旨在实现多被试协同脑视觉解码的新方法。Wills Aligner首先在解剖层面将不同被试的功能磁共振成像数据进行对齐。随后，它采用精细的脑专家混合适配器与元学习策略，以应对个体功能磁共振成像模式的差异。此外，Wills Aligner利用视觉刺激的语义关系来引导学习被试间的共性，使得每个被试的视觉解码能够从其他被试的数据中获取信息。我们在多种视觉解码任务（包括分类、跨模态检索与图像重建）上对Wills Aligner进行了严格评估。实验结果表明，Wills Aligner取得了优异的性能。