Mask-guided matting networks have achieved significant improvements and have
shown great potential in practical applications in recent years. However,
simply learning matting representation from synthetic and
lack-of-real-world-diversity matting data, these approaches tend to overfit
low-level details in wrong regions, lack generalization to objects with complex
structures and real-world scenes such as shadows, as well as suffer from
interference of background lines or textures. To address these challenges, in
this paper, we propose a novel auxiliary learning framework for mask-guided
matting models, incorporating three auxiliary tasks: semantic segmentation,
edge detection, and background line detection besides matting, to learn
different and effective representations from different types of data and
annotations. Our framework and model introduce the following key aspects: (1)
to learn real-world adaptive semantic representation for objects with diverse
and complex structures under real-world scenes, we introduce extra semantic
segmentation and edge detection tasks on more diverse real-world data with
segmentation annotations; (2) to avoid overfitting on low-level details, we
propose a module to utilize the inconsistency between learned segmentation and
matting representations to regularize detail refinement; (3) we propose a novel
background line detection task into our auxiliary learning framework, to
suppress interference of background lines or textures. In addition, we propose
a high-quality matting benchmark, Plant-Mat, to evaluate matting methods on
complex structures. Extensively quantitative and qualitative results show that
our approach outperforms state-of-the-art mask-guided methods.

我们提出了一个新颖的辅助学习框架，利用语义分割、边缘检测和背景线检测等三个辅助任务，从不同类型的数据和注释中学习不同而有效的表示，以解决合成遮罩数据所带来的过拟合、对复杂结构对象和实际场景的泛化不足、以及背景线或纹理的干扰等挑战，我们还提出了高质量的 matting 基准，Plant-Mat，实验结果表明我们的方法优于现有的 mask-guided 方法。