{"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/interactive-image-segmentation-with-latent","title":"Interactive Image Segmentation With Latent Diversity","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Zhuwen Li","Qifeng Chen","Vladlen Koltun"],"abstract":"Interactive image segmentation is characterized by multimodality. When the user clicks on a door, do they intend to select the door or the whole house? We present an end-to-end learning approach to interactive image segmentation that tackles this ambiguity. Our architecture couples two convolutional networks. The first is trained to synthesize a diverse set of plausible segmentations that conform to the user's input. The second is trained to select among these. By selecting a single solution, our approach retains compatibility with existing interactive segmentation interfaces. By synthesizing multiple diverse solutions before selecting one, the architecture is given the representational power to explore the multimodal solution space. We show that the proposed approach outperforms existing methods for interactive image segmentation, including prior work that applied convolutional networks to this problem, while being much faster.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Interactive_Image_Segmentation_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Interactive_Image_Segmentation_CVPR_2018_paper.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":"interactive-image-segmentation-with-latent","repo_url":"https://github.com/intel-isl/Intseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"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":[{"leaderboard":"/sota/interactive-segmentation-on-davis","task":"Interactive Segmentation","dataset":"DAVIS","model":"Latent diversity","rank_in_archive_order":12,"of":15,"metrics":{"NoC@85":"5.05","NoC@90":"9.57"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-grabcut","task":"Interactive Segmentation","dataset":"GrabCut","model":"Latent diversity","rank_in_archive_order":15,"of":18,"metrics":{"NoC@85":"3.20","NoC@90":"4.79"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-sbd","task":"Interactive Segmentation","dataset":"SBD","model":"Latent diversity","rank_in_archive_order":10,"of":14,"metrics":{"NoC@85":"7.41","NoC@90":"10.78"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}