论文标题
自我逆转的多尺度对比度学习，用于热面部图像的语义分割
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images
论文作者
论文摘要
热面部图像的分割是一项具有挑战性的任务。这是因为面部特征通常由于高动态的热范围场景和遮挡问题而缺乏显着性。来自不受约束的设置的数据集的有限可用性进一步限制了最先进的细分网络，损失功能和学习策略的使用，这些网络已构建和验证了RGB图像。为了应对挑战，我们提出了自我分离的多尺度对比度学习（SAM-CL）框架，作为热图像分割的新培训策略。 SAM-CL框架由SAM-CL损耗函数和热图像增强（TIAUG）模块作为特定域特异性增强技术组成。我们使用热面数据库来证明我们的方法的有效性。在现有的分割网络（UNET，注意UNET，DEEPLABV3和HRNETV2）上进行的实验证明了SAM-CL框架的一致性增长。此外，我们通过Ubcomfort和Deepbreath数据集进行了定性分析，以讨论我们提出的方法在处理无约束情况方面的作用。
Segmentation of thermal facial images is a challenging task. This is because facial features often lack salience due to high-dynamic thermal range scenes and occlusion issues. Limited availability of datasets from unconstrained settings further limits the use of the state-of-the-art segmentation networks, loss functions and learning strategies which have been built and validated for RGB images. To address the challenge, we propose Self-Adversarial Multi-scale Contrastive Learning (SAM-CL) framework as a new training strategy for thermal image segmentation. SAM-CL framework consists of a SAM-CL loss function and a thermal image augmentation (TiAug) module as a domain-specific augmentation technique. We use the Thermal-Face-Database to demonstrate effectiveness of our approach. Experiments conducted on the existing segmentation networks (UNET, Attention-UNET, DeepLabV3 and HRNetv2) evidence the consistent performance gains from the SAM-CL framework. Furthermore, we present a qualitative analysis with UBComfort and DeepBreath datasets to discuss how our proposed methods perform in handling unconstrained situations.
