Learning Multiple Representations with Inconsistency-Guided Detail
  Regularization for Mask-Guided Matting
Weihao Jiang Zhaozhi Xie Yuxiang Lu Longjie Qi Jingyong Cai Hiroyuki Uchiyama Bin Chen Yue Ding Hongtao Lu
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.