用人类感知难易度构建情绪识别训练课程，提升模型效率与鲁棒性。
CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
- 基于众包标注一致性定义样本难度，贴近人类感知规律。
- 在两种设置下均降低梯度更新次数，提升模型性能。
- 适合情绪识别、人机交互等主观任务研究者使用。
课程学习（CL）通过从简单到复杂的样本顺序训练，促进模型渐进式学习。然而，现有情绪识别中的CL方法通常依赖启发式、数据驱动或模型定义的难度，忽略了人类感知难度这一主观任务的关键因素。我们提出CHUCKLE（基于众包人类理解的课程学习框架），利用众包数据集中标注者的一致性与对齐度来定义样本难度，假设对人类而言困难的片段对神经网络同样困难。实验表明，CHUCKLE在LSTM与Transformer上均优于非课程基线，在主体相关和主体无关设置下提升性能，并减少梯度更新次数，从而增强训练效率与模型鲁棒性。
原文摘要 · Abstract (English)
Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL framework that leverages annotator agreement and alignment in crowd-sourced datasets to define sample difficulty, under the assumption that clips challenging for humans are similarly hard for neural networks. Experimental results suggest that CHUCKLE enhances the performance of LSTMs and Transformers over non-curriculum baselines, while reducing the number of gradient updates, thereby enhancing both training efficiency and model robustness in both subject-dependent and subject-independent settings.
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