Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. The inherent variability in brain function between individuals leads existing literature to focus on acquiring separate models for each individual using their respective brain signal data, ignoring commonalities between these data. In this article, we devise Psychometry, an omnifit model for reconstructing images from functional Magnetic Resonance Imaging (fMRI) obtained from different subjects. Psychometry incorporates an omni mixture-of-experts (Omni MoE) module where all the experts work together to capture the inter-subject commonalities, while each expert associated with subject-specific parameters copes with the individual differences. Moreover, Psychometry is equipped with a retrieval-enhanced inference strategy, termed Ecphory, which aims to enhance the learned fMRI representation via retrieving from prestored subject-specific memories. These designs collectively render Psychometry omnifit and efficient, enabling it to capture both inter-subject commonality and individual specificity across subjects. As a result, the enhanced fMRI representations serve as conditional signals to guide a generation model to reconstruct high-quality and realistic images, establishing Psychometry as state-of-the-art in terms of both high-level and low-level metrics.

翻译：从人脑活动中重建所观测图像通过脑机接口连接了人类视觉与计算机视觉。个体之间脑功能的固有差异性导致现有文献侧重于利用各自脑信号数据为每个个体单独建立模型，而忽略了这些数据之间的共性。在本文中，我们设计了Psychometry，一种从不同被试的功能性磁共振成像（fMRI）数据重建图像的全适配模型。Psychometry整合了全混合专家（Omni MoE）模块，其中所有专家共同工作以捕捉被试间的共性，同时每个与被试特定参数相关联的专家则处理个体差异。此外，Psychometry配备了一种检索增强的推理策略，称为Ecphory，该策略旨在通过从预存储的被试特定记忆中检索信息来增强学习到的fMRI表征。这些设计共同使Psychometry具有全适配性和高效性，使其能够同时捕捉跨被试的共性与个体特异性。因此，增强后的fMRI表征作为条件信号引导生成模型重建高质量且逼真的图像，使Psychometry在高层次和低层次指标上均达到最先进水平。