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English(EN) Cognition-Oriented Emotion Tracing from Causes to Consequences in Real-World Social Scenes

新的TRACE框架将情感建模为一个展开过程,表现优于当前AI

研究人员推出TRACE,一个旨在模拟真实社交场景中情感展开过程的新框架。该框架将情感事件形式化为三个阶段:条件(Condition)、情感(Affect)和效应(Effect),整合了可观察的线索与认知因素,如内部立场和情感表达的调控。为评估多模态模型,他们开发了TRACE-Bench,一个包含3,746个问答对、646个视频的数据集,涵盖情感识别、调控解码和因果推理等任务。研究发现人类与模型能力之间存在显著的性能差距,通用大型多模态模型表现优于专门的情感模型,尽管两者在区分显示行为与真实感受以及在生成过程中捏造事件方面都表现出反复的失败。 AI

影响 这项研究可能带来AI对人类情感更细致的理解,从而改进人机交互和情感计算应用。

排序理由 该集群包含一篇详细介绍AI情感追踪新框架和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的TRACE框架将情感建模为一个展开过程,表现优于当前AI

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该集群包含一篇详细介绍AI情感追踪新框架和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hao Li, Jinye Zhang, Bobo Li, Mong-Li Lee, Wynne Hsu, Zheng Wang, Hao Fei, Min Zhang ·

    面向认知的现实世界社交场景情感追踪:从原因到结果

    arXiv:2610.11410v1 Announce Type: new Abstract: Affective computing has progressed from categorical emotion recognition to open-ended affective analysis with large multimodal models. Yet affective science describes emotion as an unfolding process shaped by appraisal, regulation, …