低成本视频-时间先验作为熟悉视频上EEG-fNIRS情绪回归的强基线
Low-Cost Video--Time Priors as a Strong Baseline for EEG--fNIRS Emotion Regression on Familiar Videos
浏览论文内容
中文总结 AI 辅助
本文提出视频-时间先验作为熟悉视频情绪回归的强基线，通过消融实验证明其低成本高效性，而EEG-fNIRS仅作为可选残差信号。
中文摘要 AI 辅助
连续情绪回归在观看者观看视频时估计逐时刻的效价和唤醒度。在熟悉视频部署中，来自训练参与者的响应估计，以及先验主导的固定融合测试，检验生理信号是否提供残差校正。在24名受试者上的五折受试者留出评估中，内部和外部评估中分别与融合的MAE相差在0.05和0.32以内。源消融实验表明，视频身份和视频内时间占误差减少的大部分，而EEG-fNIRS的增益较小且在不同参与者和视频间存在差异。这些结果将视频-时间先验识别为强且低成本的基线，并将EEG-fNIRS定位为熟悉视频情绪回归的可选残差信号。
英文摘要
Continuous emotion regression estimates moment-to-moment valence and arousal while a viewer watches a video. In familiar-video deployment, responses fron training participant-specific estimate, and prior-dominating fixed fusion tests whether physiology adds residual correction. In five-fold subject-held-out evaluation on 24was within 0.05 and 0.32 MAE of fusion in the internal and external evaluations, respectively. Source-explicit ablations showed that video identity and within-video tine accounted for most of the reduction, while EG-FNIRS gains were smaller and varied across participants and videos. These results identify the video-time prior as a strong, low-cost baseline and position EEG-fNIRS as an optional residual signal for familiar-video emotion regression.
发表机构
- Sun Yat-sen University（中山大学）
- Pengcheng Laboratory（鹏城实验室）
机构由 AI 辅助整理，请以论文原文为准。
