arXiv 2512.047282025-12-04cs.CVcs.AI

测量未言之: 一种解耦模型和基准用于野外心理分析

Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild

College of Computer Science, National University of Defense Technology（计算机科学学院，国防科技大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）

Yigui Feng, Qinglin Wang, Haotian Mo, Yang Liu, Ke Liu, Gencheng Liu, Xinhai Chen, Siqi Shen, Songzhu Mei, Jie Liu

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AI总结：

本文提出MIND模型和PRISM基准，通过解耦算法和专家标注数据提升野外对话心理分析的准确性与可靠性。

摘要：

生成性心理分析在真实环境中面临两个根本挑战：(1)现有视觉-语言模型(VLMs)无法解决发音-情感歧义，其中语音的视觉模式模仿情绪表达；(2)进展受缺乏可验证的评估指标限制，这些指标能够评估视觉基础和推理深度。我们提出一个完整的生态系统来解决这两个挑战。首先，我们引入了多级洞察网络解耦(MIND)，一种新颖的分层视觉编码器，引入了状态判断模块，通过其时间特征方差来算法抑制模糊的嘴唇特征，实现显式的视觉解耦。其次，我们构建了ConvoInsight-DB，一个具有专家标注的大型数据集，用于微表情和深层心理推断。第三，我们设计了Mental Reasoning Insight Rating Metric (PRISM)，一种自动化维度框架，利用专家引导的LLM来衡量大心理视觉模型的多维性能。在我们的PRISM基准上，MIND显著优于所有基线，实现了比先前SOTA在微表情检测上的+86.95%的提升。消融研究证实，我们的状态判断解耦模块是这一性能飞跃最关键的组件。我们的代码已开源。

英文摘要：

Generative psychological analysis of in-the-wild conversations faces two fundamental challenges: (1) existing Vision-Language Models (VLMs) fail to resolve Articulatory-Affective Ambiguity, where visual patterns of speech mimic emotional expressions; and (2) progress is stifled by a lack of verifiable evaluation metrics capable of assessing visual grounding and reasoning depth. We propose a complete ecosystem to address these twin challenges. First, we introduce Multilevel Insight Network for Disentanglement(MIND), a novel hierarchical visual encoder that introduces a Status Judgment module to algorithmically suppress ambiguous lip features based on their temporal feature variance, achieving explicit visual disentanglement. Second, we construct ConvoInsight-DB, a new large-scale dataset with expert annotations for micro-expressions and deep psychological inference. Third, Third, we designed the Mental Reasoning Insight Rating Metric (PRISM), an automated dimensional framework that uses expert-guided LLM to measure the multidimensional performance of large mental vision models. On our PRISM benchmark, MIND significantly outperforms all baselines, achieving a +86.95% gain in micro-expression detection over prior SOTA. Ablation studies confirm that our Status Judgment disentanglement module is the most critical component for this performance leap. Our code has been opened.

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