首个面向增量微表情识别的基准测试，助力模型持续学习新数据。
A Benchmark for Incremental Micro-expression Recognition
- 设计专用于微表情的增量学习框架，模拟真实场景数据流。
- 构建有序数据集并定义两种交叉评估协议，支持多目标测试。
- 提供6种基线方法及结果，适合持续学习与情绪识别研究者使用。
微表情识别在理解隐藏情绪方面具有关键作用，应用广泛。传统方法假设所有训练数据一次性可用，但实际场景中数据持续演进。为应对需适应新数据同时保留旧知识的需求，我们提出首个专为增量微表情识别设计的基准。贡献包括：首先，构建适配微表情识别的增量学习设定；其次，整理按精心设计顺序排列的序列数据集，贴近真实场景；第三，定义两种基于交叉验证的测试协议，分别针对不同评估目标；最后，提供六种基线方法及其评估结果。该基准为推进增量微表情识别研究奠定基础。本研究所有源代码将公开于 https://github.com/ZhengQinLai/IMER-benchmark。
原文摘要 · Abstract (English)
Micro-expression recognition plays a pivotal role in understanding hidden emotions and has applications across various fields. Traditional recognition methods assume access to all training data at once, but real-world scenarios involve continuously evolving data streams. To respond to the requirement of adapting to new data while retaining previously learned knowledge, we introduce the first benchmark specifically designed for incremental micro-expression recognition. Our contributions include: Firstly, we formulate the incremental learning setting tailored for micro-expression recognition. Secondly, we organize sequential datasets with carefully curated learning orders to reflect real-world scenarios. Thirdly, we define two cross-evaluation-based testing protocols, each targeting distinct evaluation objectives. Finally, we provide six baseline methods and their corresponding evaluation results. This benchmark lays the groundwork for advancing incremental micro-expression recognition research. All source code used in this study will be publicly available at https://github.com/ZhengQinLai/IMER-benchmark.
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