Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917624742412288 |
|---|---|
| author | Jiang, Weihao Xie, Zhaozhi Lu, Yuxiang Qi, Longjie Cai, Jingyong Uchiyama, Hiroyuki Chen, Bin Ding, Yue Lu, Hongtao |
| author_facet | Jiang, Weihao Xie, Zhaozhi Lu, Yuxiang Qi, Longjie Cai, Jingyong Uchiyama, Hiroyuki Chen, Bin Ding, Yue Lu, Hongtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting Jiang, Weihao Xie, Zhaozhi Lu, Yuxiang Qi, Longjie Cai, Jingyong Uchiyama, Hiroyuki Chen, Bin Ding, Yue Lu, Hongtao Computer Vision and Pattern Recognition 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. |
| title | Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.19213 |