ReFace：重新组织面部时空表征以改进疼痛评估
ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment
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中文总结 AI 辅助
研究针对面部视频自动疼痛评估难题，提出ReFace方法，通过将面部划分为四个空间象限进行令牌化处理，在AI4Pain数据集测试中达56.00%准确率，在固定基准协议下优于其他方法，证明空间重组可提升性能，单象限区域成本低且具竞争力。
中文摘要 AI 辅助
由于与疼痛相关的面部线索存在空间异质性，从面部视频中进行自动疼痛评估仍然具有挑战性。本研究提出了ReFace，这是一种空间重组管道，在进行令牌化之前将面部输入划分为四个空间象限，而不是将整个面部作为一个区域来处理。在AI4Pain数据集上进行评估，该方法仅使用视频在测试集上达到了56.00%的准确率，在所比较的方法中在固定的AI4Pain基准协议下实现了最高的报告准确率。值得注意的是，四象限配置处理的总像素预算与全脸输入相同，但实现了更高的准确率，这表明空间重组在所提出的令牌化设计下可以提高性能。一个仅处理四分之一像素的单象限区域，以一小部分计算成本仍具有竞争力。
英文摘要
Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace, a spatial reorganization pipeline that divides facial input into four spatial quadrants before tokenization, rather than processing the entire face as a single region. Evaluated on the AI4Pain dataset, the proposed approach achieves $56.00\%$ accuracy on the test set using video only, achieving the highest reported accuracy under the fixed AI4Pain benchmark protocol among the compared methods. Notably, the four-quadrant configuration processes the same total pixel budget as the full-face input, yet achieves higher accuracy, suggesting that spatial reorganization can improve performance under the proposed tokenization design. A single quadrant region, processing just one quarter of those pixels, remains competitive at a fraction of the computational cost.
