  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.
