{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spg-net-segmentation-prediction-and-guidance","title":"SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting","arxiv_id":"1805.03356","date":"2018-05-09","proceeding":null,"authors":["Yuhang Song","Chao Yang","Yeji Shen","Peng Wang","Qin Huang","C. -C. Jay Kuo"],"abstract":"In this paper, we focus on image inpainting task, aiming at recovering the\nmissing area of an incomplete image given the context information. Recent\ndevelopment in deep generative models enables an efficient end-to-end framework\nfor image synthesis and inpainting tasks, but existing methods based on\ngenerative models don't exploit the segmentation information to constrain the\nobject shapes, which usually lead to blurry results on the boundary. To tackle\nthis problem, we propose to introduce the semantic segmentation information,\nwhich disentangles the inter-class difference and intra-class variation for\nimage inpainting. This leads to much clearer recovered boundary between\nsemantically different regions and better texture within semantically\nconsistent segments. Our model factorizes the image inpainting process into\nsegmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two\nsteps, which predict the segmentation labels in the missing area first, and\nthen generate segmentation guided inpainting results. Experiments on multiple\npublic datasets show that our approach outperforms existing methods in\noptimizing the image inpainting quality, and the interactive segmentation\nguidance provides possibilities for multi-modal predictions of image\ninpainting.","url_abs":"http://arxiv.org/abs/1805.03356v4","url_pdf":"http://arxiv.org/pdf/1805.03356v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spg-net-segmentation-prediction-and-guidance","repo_url":"https://github.com/yccyenchicheng/pytorch-SegInpaint","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"interactive-segmentation","task_name":"Interactive Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.03356","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}