Image harmonization is a crucial technique in image composition that aims to
seamlessly match the background by adjusting the foreground of composite
images. Current methods adopt either global-level or pixel-level feature
matching. Global-level feature matching ignores the proximity prior, treating
foreground and background as separate entities. On the other hand, pixel-level
feature matching loses contextual information. Therefore, it is necessary to
use the information from semantic maps that describe different objects to guide
harmonization. In this paper, we propose Semantic-guided Region-aware Instance
Normalization (SRIN) that can utilize the semantic segmentation maps output by
a pre-trained Segment Anything Model (SAM) to guide the visual consistency
learning of foreground and background features. Abundant experiments
demonstrate the superiority of our method for image harmonization over
state-of-the-art methods.

本文提出了一种利用语义分割地图指导前景和背景特征的视觉一致性学习的方法，称为语义引导的区域感知实例标准化（SRIN），并且通过丰富的实验验证了该方法在图像协调中的优越性。