Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer

• URL: http://arxiv.org/abs/2109.08024v1
• Date: Tue, 14 Sep 2021 09:40:25 GMT
• Title: Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer
• Authors: Fushun Zhu, Shan Zhao, Peng Wang, Hao Wang, Hua Yan, Shuaicheng Liu
• Abstract summary: We propose a semi-supervised network for wide-angle portraits correction. Our network, named as Multi-Scale Swin-Unet (MS-Unet), is built upon the multi-scale swin transformer block (MSTB)
• Score: 17.455782652441187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a semi-supervised network for wide-angle portraits correction. Wide-angle images often suffer from skew and distortion affected by perspective distortion, especially noticeable at the face regions. Previous deep learning based approaches require the ground-truth correction flow maps for the training guidance. However, such labels are expensive, which can only be obtained manually. In this work, we propose a semi-supervised scheme, which can consume unlabeled data in addition to the labeled data for improvements. Specifically, our semi-supervised scheme takes the advantages of the consistency mechanism, with several novel components such as direction and range consistency (DRC) and regression consistency (RC). Furthermore, our network, named as Multi-Scale Swin-Unet (MS-Unet), is built upon the multi-scale swin transformer block (MSTB), which can learn both local-scale and long-range semantic information effectively. In addition, we introduce a high-quality unlabeled dataset with rich scenarios for the training. Extensive experiments demonstrate that the proposed method is superior over the state-of-the-art methods and other representative baselines.

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