提出首个面向真实对话场景的微表情分析框架，提升真实环境下的检测精度。
MELDAE: A Framework for Micro-Expression Spotting, Detection, and Automatic Evaluation in In-the-Wild Conversational Scenes
- 构建首个自然对话场景下的微表情数据集，贴近真实应用。
- 在WDMD数据集上将关键定位指标提升17.72%，显著优于现有方法。
- 适合情绪分析、人机交互与真实场景视频理解的研究者使用。
准确分析自发、无意识的微表情对揭示真实情感至关重要，但在自然对话等真实场景中仍具挑战性。现有研究多依赖受控实验室数据，真实环境下性能大幅下降。为此，本文提出三项贡献：首个聚焦自然对话场景的微表情数据集；一个端到端的定位与检测框架MELDAE；以及一种新型边界感知损失函数，通过惩罚起始与结束时刻误差提升时间定位精度。大量实验表明，该框架在WDMD数据集上达到当前最优表现，关键定位指标F1_{DR}相比最强基线提升17.72%，且在已有基准上展现出优异泛化能力。
原文摘要 · Abstract (English)
Accurately analyzing spontaneous, unconscious micro-expressions is crucial for revealing true human emotions, but this task remains challenging in wild scenarios, such as natural conversation. Existing research largely relies on datasets from controlled laboratory environments, and their performance degrades dramatically in the real world. To address this issue, we propose three contributions: the first micro-expression dataset focused on conversational-in-the-wild scenarios; an end-to-end localization and detection framework, MELDAE; and a novel boundary-aware loss function that improves temporal accuracy by penalizing onset and offset errors. Extensive experiments demonstrate that our framework achieves state-of-the-art results on the WDMD dataset, improving the key F1_{DR} localization metric by 17.72% over the strongest baseline, while also demonstrating excellent generalization capabilities on existing benchmarks.
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