区域感知实例一致性学习用于微表情识别
Region-Aware Instance Consistency Learning for Micro-Expression Recognition
提出无需顶点帧标注的微表情识别范式，利用起始帧与近中值帧构建运动实例，并通过实例区域一致性模块强制相似面部激活区域的注意力一致，为细微运动分析提供了可复用的弱监督思路。
原文依据 our method treats each sequence as a set of multiple onset/near-median motion instances
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论微表情识别（MER）因运动细微而具有挑战性。现有方法严重依赖起始/顶点帧对来捕捉最具判别力的运动线索。该范式面临顶点标注劳动密集和数据利用效率低下的问题。本文提出一种新的MER范式，无需昂贵的顶点标注即可有效捕捉细微运动动态。我们的关键洞察是：序列中的帧相对于起始帧表现出空间一致且强度变化的运动线索。受此启发，我们的方法将每个序列视为多个起始/近中值运动实例的集合。为充分利用这些不同实例所传达的较弱运动信息，我们的框架引入实例区域一致性（IRC）模块，该模块在同一集合内不同实例的相似面部激活区域上强制视觉注意力一致性。此外，我们提出多区域发现（MRD）模块，结合自监督学习将注意力扩展到通常被忽略的更细微激活区域。在四个公开微表情数据集上的大量实验表明，我们提出的方法在未使用任何顶点帧标注的情况下超越了最优方法。
英文原文摘要 1314 字符
Micro-expression Recognition (MER) is challenging due to the subtle motion. Existing methods heavily rely on the onset/apex pair to capture the most discriminative motion clues. This paradigm struggles with labor-intensive apex annotation and effective utilization of data. In this paper, we propose a novel paradigm for MER that eliminates the need for expensive apex annotations while effectively capturing subtle motion dynamics. Our key insight is that frames within the sequence exhibit spatially consistent and intensity varied motion cues relative to the onset frame. Motivated by this, our method treats each sequence as a set of multiple onset/near-median motion instances. To fully exploit weaker motion information conveyed by these varied instances, our framework introduces an Instance Region Consistency (IRC) module that enforces visual attention consistency on similar facial activation regions across different instances within the same set. Furthermore, we present a Multi-Region Discovery (MRD) module with self-supervised learning to expand attention on more subtle activation regions which are typically neglected. Extensive experiments on four public micro-expression datasets demonstrate that our proposed approach surpasses state-of-the-art methods without using any apex frame annotations.