SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization

• URL: http://arxiv.org/abs/2204.13962v1
• Date: Fri, 29 Apr 2022 09:22:01 GMT
• Title: SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization
• Authors: Yucheng Hang, Bin Xia, Wenming Yang, Qingmin Liao
• Abstract summary: We propose a self-consistent style contrastive learning scheme (SCS-Co) for image harmonization. By dynamically generating multiple negative samples, our SCS-Co can learn more distortion knowledge and well regularize the generated harmonized image. In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution.
• Score: 29.600429707123645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only use the real image as the positive sample to guide the training, and at most introduce the corresponding composite image as a single negative sample for an auxiliary constraint, which leads to limited distortion knowledge, and further causes a too large solution space, making the generated harmonized image distorted. Besides, none of them jointly constrain from the foreground self-style and foreground-background style consistency, which exacerbates this problem. Moreover, recent region-aware adaptive instance normalization achieves great success but only considers the global background feature distribution, making the aligned foreground feature distribution biased. To address these issues, we propose a self-consistent style contrastive learning scheme (SCS-Co). By dynamically generating multiple negative samples, our SCS-Co can learn more distortion knowledge and well regularize the generated harmonized image in the style representation space from two aspects of the foreground self-style and foreground-background style consistency, leading to a more photorealistic visual result. In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity. Experiments demonstrate the superiority of our method over other state-of-the-art methods in both quantitative comparison and visual analysis.

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