论文标题
唯一基于集体组的多标签平衡优化器用于动作单元检测
Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection
论文作者
论文摘要
单标签数据的平衡方法不能应用于多标签问题，因为它们还将重新置于出现很高的样本。我们建议将这个问题重新制定为优化问题，以平衡多标签数据。我们将这种平衡算法应用于培训数据集，以检测孤立的面部运动，所谓的动作单元。几个动作单位可以描述组合的情绪或身体状态，例如疼痛。由于该领域的数据集有限且大多是不平衡的，因此我们展示了如何优化平衡，然后增强可以改善动作单元检测。在2020年面部和手势识别的IEEE会议上，我们在野外行为分析（ABAW）挑战中对动作单元检测任务排名第三。
Balancing methods for single-label data cannot be applied to multi-label problems as they would also resample the samples with high occurrences. We propose to reformulate this problem as an optimization problem in order to balance multi-label data. We apply this balancing algorithm to training datasets for detecting isolated facial movements, so-called Action Units. Several Action Units can describe combined emotions or physical states such as pain. As datasets in this area are limited and mostly imbalanced, we show how optimized balancing and then augmentation can improve Action Unit detection. At the IEEE Conference on Face and Gesture Recognition 2020, we ranked third in the Affective Behavior Analysis in-the-wild (ABAW) challenge for the Action Unit detection task.
