SATTC：面向跨被试脑电到图像检索的结构感知无标签测试时校准
SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval
提供了一种无标签的测试时校准头，直接作用于冻结编码器的相似度矩阵，通过白化与CSLS变体缓解跨域检索中的hubness问题，可复用于视觉-语言跨模态检索或跨域检索的排序稳定化。
原文依据 a label-free calibration head that operates directly on the similarity matrix of frozen EEG and image encoders
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论跨被试脑电到图像的视觉解码检索面临被试偏移和嵌入空间中的hubness问题，这些问题扭曲了相似度几何结构并破坏了top-k排序的稳定性，使得小k候选列表不可靠。我们提出SATTC（结构感知测试时校准），一种无标签校准头，直接作用于冻结的脑电和图像编码器的相似度矩阵。SATTC将脑电嵌入的几何专家被试自适应白化与跨域相似度局部缩放（CSLS）的自适应变体相结合，并构建了一个由互最近邻、双向top-k排名和类别流行度组成的结构专家，通过简单的专家乘积规则进行融合。在THINGS-EEG数据集上，采用严格的留一被试协议，使用余弦相似度、l2归一化嵌入和候选白化的标准化推理已经比原始ATM检索设置产生了强大的跨被试基线。在此基线基础上，SATTC进一步提高了Top-1和Top-5准确率，减少了hubness和每类不平衡，并产生了更可靠的小k候选列表。这些增益在多个脑电编码器上可迁移，支持SATTC作为跨被试神经解码的编码器无关、无标签测试时校准层。代码见https://github.com/QunjieHuang/SATTC-CVPR2026。
英文原文摘要 1349 字符
Cross-subject EEG-to-image retrieval for visual decoding is challenged by subject shift and hubness in the embedding space, which distort similarity geometry and destabilize top-k rankings, making small-k shortlists unreliable. We introduce SATTC (Structure-Aware Test-Time Calibration), a label-free calibration head that operates directly on the similarity matrix of frozen EEG and image encoders. SATTC combines a geometric expert-subject-adaptive whitening of EEG embeddings with an adaptive variant of Cross-domain Similarity Local Scaling (CSLS)--and a structural expert built from mutual nearest neighbors, bidirectional top-k ranks, and class popularity, fused via a simple Product-of-Experts rule. On THINGS-EEG under a strict leave-one-subject-out protocol, standardized inference with cosine similarities, l2-normalized embeddings, and candidate whitening already yields a strong cross-subject baseline over the original ATM retrieval setup. Building on this baseline, SATTC further improves Top-1 and Top-5 accuracy, reduces hubness and per-class imbalance, and produces more reliable small-k shortlists. These gains transfer across multiple EEG encoders, supporting SATTC as an encoder-agnostic, label-free test-time calibration layer for cross-subject neural decoding. Code is available at https://github.com/QunjieHuang/SATTC-CVPR2026