针对情绪识别域适应中的模糊标签问题，提出新型抗噪损失函数。
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition
- 从模糊性视角设计抗噪损失，聚焦未预测类别调整
- 在3个数据集26个任务上显著提升适应性能
- 适合关注隐私保护的情绪识别部署场景
视觉情绪识别中的无源域适应（SFDA-VER）是一项极具挑战性的任务，要求在不依赖源数据的情况下将情绪识别模型适配到目标域，对数据隐私保护具有重要意义。然而，由于情绪数据与传统图像分类数据存在显著差异，现有无源域适应方法在此任务上表现不佳。本文从模糊性角度分析该任务，识别出两个关键问题：情绪标签的模糊性与伪标签的模糊性，分别源于情绪标注的固有不确定性及伪标签可能产生的误判。为此，我们提出一种新颖的模糊感知损失（FAL），使情绪识别模型在模糊标签下仍能有效学习与适应。具体而言，FAL改进标准交叉熵损失，重点调节未被预测类别的损失，避免大量不确定或错误预测在适应过程中主导模型训练。此外，我们对FAL进行了理论分析，证明其对生成伪标签中噪声的鲁棒性。在三个基准数据集上的26个域适应子任务上进行的大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Source-free domain adaptation in visual emotion recognition (SFDA-VER) is a highly challenging task that requires adapting VER models to the target domain without relying on source data, which is of great significance for data privacy protection. However, due to the unignorable disparities between visual emotion data and traditional image classification data, existing SFDA methods perform poorly on this task. In this paper, we investigate the SFDA-VER task from a fuzzy perspective and identify two key issues: fuzzy emotion labels and fuzzy pseudo-labels. These issues arise from the inherent uncertainty of emotion annotations and the potential mispredictions in pseudo-labels. To address these issues, we propose a novel fuzzy-aware loss (FAL) to enable the VER model to better learn and adapt to new domains under fuzzy labels. Specifically, FAL modifies the standard cross entropy loss and focuses on adjusting the losses of non-predicted categories, which prevents a large number of uncertain or incorrect predictions from overwhelming the VER model during adaptation. In addition, we provide a theoretical analysis of FAL and prove its robustness in handling the noise in generated pseudo-labels. Extensive experiments on 26 domain adaptation sub-tasks across three benchmark datasets demonstrate the effectiveness of our method.
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