论文标题
重新审查面部表情识别的自我监督对比学习
Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
论文作者
论文摘要
最先进的面部表达识别的成功在很大程度上取决于大规模注释的数据集。但是，它在获取面部表达数据集的清洁和一致注释方面构成了巨大挑战。另一方面，由于其简单而有效的实例歧视培训策略，自我监督的对比学习已获得了很大的知名度，这可能会避免注释问题。然而，实例级别的歧视仍然存在固有的劣势，面对复杂的面部表征，这更具挑战性。在本文中，我们重新审视使用自我监督的对比学习，并探索三种核心策略，以执行特定表达的表示并最大程度地减少来自其他面部属性（例如身份和面部样式）的干扰。实验结果表明，我们所提出的方法在分类和维面表达识别任务方面优于当前最新的自我监督学习方法。
The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets. However, it poses great challenges in acquiring clean and consistent annotations for facial expression datasets. On the other hand, self-supervised contrastive learning has gained great popularity due to its simple yet effective instance discrimination training strategy, which can potentially circumvent the annotation issue. Nevertheless, there remain inherent disadvantages of instance-level discrimination, which are even more challenging when faced with complicated facial representations. In this paper, we revisit the use of self-supervised contrastive learning and explore three core strategies to enforce expression-specific representations and to minimize the interference from other facial attributes, such as identity and face styling. Experimental results show that our proposed method outperforms the current state-of-the-art self-supervised learning methods, in terms of both categorical and dimensional facial expression recognition tasks.
