构建多视角社交意图数据集，助力智能系统理解人类主观判断差异。
COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention
- 设计新标注流程，捕捉观察者个人解读倾向对意图判断的影响。
- 分析意图叙事的多样性与合理性，验证多视角解释的有效性。
- 提供带隐私保护的多模态数据，适合研究社交互动与行为建模。
COSI-Lab 提出一个跨学科国际会议中32位学者参与的多模态、多传感器数据集，包含两个30分钟弱脚本化交流环节，具有真实职业与社交后果。研究认为，未来智能系统应将主观感知多样性视为可解释的视角驱动推理过程，而非标签噪声。聚焦于外部观察者判定的表面意图推断（AII）问题，将意图定义为独立于实际未来结果。贡献包括：1. 首创考虑观察者自身解读偏好的AII标注流程；2. 对意图叙事进行定量与定性分析，涵盖多样性、语境关联性与合理性；3. 建立AII及周边上下文因素（如社交参与度）的基准任务；4. 提供所有参与者语音质量音频与隐私保护的多模态数据，支持语言与非语言行为分析；5. 将参与者自报目标（30分钟至3小时）与标注的AII（秒级）进行耦合。
原文摘要 · Abstract (English)
COSI-Lab presents a multimodal, multi-sensor dataset of an interdisciplinary scientific workshop containing 32 academics at an international conference. It captures ecologically valid social interactions in a weakly scripted setting consisting of two 30-minute mingling sessions with real professional and social consequences for the participants involved. We argue that future intelligent systems could be better equipped to handle subjective perceptions by modeling their multiplicity not as label noise but as a explainable perspective-driven reasoning process. We focus on the Apparent Intent Inference (AII) problem as determined by ex-situ observers and conceptualize intentions to be independent of manifest future outcomes. We contribute 1. a novel annotation process for AII that accounts for a perceiver's own interpretative tendencies, 2. quantitative and qualitative analyses of intent narratives with respect to diversity, grounding, and plausibility; 3. benchmark tasks for AII and surrounding relevant contextual factors such as social involvement; 4. speech quality audio for all participants as well as privacy preserving multi-modal data, enabling lexical and nonverbal behavior analysis; and 5. coupling of self-reported goals of each participant (30 minute to 3 hour) with annotated AII (seconds).
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