发表机构
IT University of Copenhagen; GN Advanced Science, GN Hearing（哥本哈根信息技术大学; GN 先进科学，GN 听力）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用GroupAffect-4数据集，提出伪标签增强方法以应对协作群体情感感知中的稀疏标签问题，实验表明该方法优于仅标签基线，且人格信息宜作为团队内选择机制。
AI 中文摘要
在自然群体互动中的生理情感感知通常受限于稀疏标签而非传感器数据：可穿戴设备产生大量时间窗口，而自我报告每节仅收集几次。利用GroupAffect-4（一个包含可穿戴生理、眼动追踪、大五人格和任务后VAD标签的四人群组协作数据集），我们研究了稀疏监督下的伪标签增强。我们在共享目标构建流程中比较了无增强、高斯过程伪标签、人格感知信任加权以及人格加置信度联合加权。结果表明，在已知团队设置中，伪标签增强优于仅使用标签的基线。然而，大五余弦相似度的狭窄范围（0.91-0.99）使得细粒度人格加权无效；人格相似度主要作为同团队过滤器而非校准的信任信号。经平滑处理后，增强的SVM变体在效价和唤醒度上基本持平，而人格加置信度联合变体在支配度上得分最高。跨受试者LOSO迁移仍令人鼓舞，尤其是唤醒度，而严格的会话隔离LOGO则消除了增强收益。鉴于仅10个群体，LOGO应被解释为未见群体迁移的保守下界。总体而言，结果表明伪标签增强可以更好地利用稀疏标注的协作情感数据，而人格信息最有用的是作为团队内选择机制。
英文摘要
Physiological affect sensing in naturalistic group interaction is often limited by sparse labels rather than sensor data: wearable devices produce many time windows, while self-reports are collected only a few times per session. Using GroupAffect-4, a four-person collaborative dataset with wearable physiology, eye tracking, Big Five personality, and post-task VAD labels, we study pseudo-label augmentation for affect sensing under sparse supervision. We compare no augmentation, Gaussian Process pseudo-labelling, personality-aware trust weighting, and joint personality-plus-confidence weighting within a shared target-construction pipeline. Results show that pseudo-label augmentation improves over the labelled-only baseline in the known-team setting. However, the narrow range of Big Five cosine similarities (0.91-0.99) makes fine-grained personality weighting ineffective; personality similarity functions mainly as a same-team filter rather than a calibrated trust signal. With smoothing, augmented SVM variants are effectively tied on Valence and Arousal, while the joint personality-plus-confidence variant gives the highest Dominance score. Cross-subject LOSO transfer remains encouraging, especially for Arousal, whereas strict session-isolated LOGO removes the augmentation benefit. Given only 10 groups, LOGO should be interpreted as a conservative lower bound on unseen-group transfer. Overall, the results suggest that pseudo-label augmentation can make better use of sparsely labelled collaborative affect data, while personality information is most useful as a within-team selection mechanism.
