Semantic image segmentation aims to obtain object labels with precise boundaries, which usually suffers from overfitting. Recently, various data augmentation strategies like regional dropout and mix strategies have been proposed to address the problem. These strategies have proved to be effective for guiding the model to attend on less discriminative parts. However, current strategies operate at the image level, and objects and the background are coupled. Thus, the boundaries are not well augmented due to the fixed semantic scenario. In this paper, we propose ObjectAug to perform object-level augmentation for semantic image segmentation. ObjectAug first decouples the image into individual objects and the background using the semantic labels. Next, each object is augmented individually with commonly used augmentation methods (e.g., scaling, shifting, and rotation). Then, the black area brought by object augmentation is further restored using image inpainting. Finally, the augmented objects and background are assembled as an augmented image. In this way, the boundaries can be fully explored in the various semantic scenarios. In addition, ObjectAug can support category-aware augmentation that gives various possibilities to objects in each category, and can be easily combined with existing image-level augmentation methods to further boost performance. Comprehensive experiments are conducted on both natural image and medical image datasets. Experiment results demonstrate that our ObjectAug can evidently improve segmentation performance.
翻译:语义图像分割图旨在获取具有精确边界的物体标签,这些标记通常存在过度设置。最近,提出了各种数据增强策略,例如区域退位和混合策略,以解决这一问题。这些策略已证明对引导模型处理不那么具有歧视性的部分十分有效。然而,当前的策略在图像层面运作,而对象和背景是结合的。因此,由于固定语义假设,边界没有很好地扩大。在本文件中,我们提议“对象Aug”为语义图像分割进行目标级增强。“对象Aug”首先用语义标签将图像分解为单个对象和背景。接下来,每个对象都通过常用的增强方法(例如,缩放、移动和旋转)单独增强。然后,目标增强带来的黑区域将使用图像部分进一步恢复。最后,由于固定语义假设的情景,扩展对象和背景将组成一个增强的图像。通过这种方式,可以充分探索各种语义图像分区的边界。此外,“对象”可以支持类别识别增强能力,使每个类别中的物体都以常用的增强性能提高整个图像水平。可以很容易地将现有的实验结果与现有分析结果结合起来。