SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network

• Published in: Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 14518 - 14527
• Main Authors: Huynh, Chuong, Zhou, Yuqian, Lin, Zhe, Barnes, Connelly, Shechtman, Eli, Amirghodsi, Sohrab, Shrivastava, Abhinav
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2023
• Subjects: Cleaning; Computer vision; grouping and shape analysis; Image quality; Object segmentation; Pipelines; Segmentation; Transformers; Visualization
• ISSN: 1063-6919
• DOI: 10.1109/CVPR52729.2023.01395

In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to achieve the task with just a single click. Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation and then selecting the segments containing the clicks. We also showcase how a transformer-based module can be used to identify more distracting regions similar to the user's click position. Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups. By significantly simplifying the photo cleaning and retouching process, our proposed model provides inspiration for exploring rare object segmentation and group selection with a single click. More information can be found at https://github.com/hmchuong/SimpSON.