不取平均标注，用共识网络提升连续情绪识别效果
Learning Annotation Consensus for Continuous Emotion Recognition
- 用共识网络融合多标注者数据，生成统一表征
- 在RECOLA和COGNIMUSE上优于单标签统一方法
- 适合标注不一致但数据丰富的场景
在情感计算中，数据集常包含多个标注者的注释，彼此间可能存在分歧。传统做法是将这些注释合并为单一标准标签，可能丢失重要的标注者间差异信息。本文提出一种多标注者训练方法用于连续情绪识别（CER），旨在从所有标注者中学习共识，而非依赖单一参考标签。该方法通过共识网络聚合多标注，生成统一表示，指导主模型更好地反映集体输入。在RECOLA和COGNIMUSE数据集上的实验表明，该方法显著优于将标注统一为单标签的传统方法。结果证明充分挖掘多标注数据的价值，并展示了其在标注丰富但不一致的各类场景中的适用性。
原文摘要 · Abstract (English)
In affective computing, datasets often contain multiple annotations from different annotators, which may lack full agreement. Typically, these annotations are merged into a single gold standard label, potentially losing valuable inter-rater variability. We propose a multi-annotator training approach for continuous emotion recognition (CER) that seeks a consensus across all annotators rather than relying on a single reference label. Our method employs a consensus network to aggregate annotations into a unified representation, guiding the main arousal-valence predictor to better reflect collective inputs. Tested on the RECOLA and COGNIMUSE datasets, our approach outperforms traditional methods that unify annotations into a single label. This underscores the benefits of fully leveraging multi-annotator data in emotion recognition and highlights its applicability across various fields where annotations are abundant yet inconsistent.
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