Decoding visual information from human brain activity has seen remarkable
advancements in recent research. However, due to the significant variability in
cortical parcellation and cognition patterns across subjects, current
approaches personalized deep models for each subject, constraining the
practicality of this technology in real-world contexts. To tackle the
challenges, we introduce Wills Aligner, a robust multi-subject brain
representation learner. Our Wills Aligner initially aligns different subjects'
brains at the anatomical level. Subsequently, it incorporates a mixture of
brain experts to learn individual cognition patterns. Additionally, it
decouples the multi-subject learning task into a two-stage training, propelling
the deep model and its plugin network to learn inter-subject commonality
knowledge and various cognition patterns, respectively. Wills Aligner enables
us to overcome anatomical differences and to efficiently leverage a single
model for multi-subject brain representation learning. We meticulously evaluate
the performance of our approach across coarse-grained and fine-grained visual
decoding tasks. The experimental results demonstrate that our Wills Aligner
achieves state-of-the-art performance.

Wills Aligner 是一个强大的多主体大脑表征学习器，通过解决解剖差异问题，实现了在多主体中高效利用单个模型进行脑表征学习，并在粗粒度和细粒度的视觉解码任务上取得了最先进的表现。