{"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-object-segmentation-with-inside","title":"Interactive Object Segmentation With Inside-Outside Guidance","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Shiyin Zhang"," Jun Hao Liew"," Yunchao Wei"," Shikui Wei"," Yao Zhao"],"abstract":"This paper explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose an Inside-Outside Guidance (IOG) approach in this work. Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of a tight bounding box that encloses the target object. This results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG is four-fold: 1) the two outside points can help to remove distractions from other objects or background; 2) the inside point can help to eliminate the unrelated regions inside the bounding box; 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR in labeling some extreme samples; 4) our approach naturally supports additional clicks annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks, but also demonstrates strong generalization capability across different domains such as street scenes, aerial imagery and medical images, without fine-tuning. In addition, we also propose a simple two-stage solution that enables our IOG to produce high quality instance segmentation masks from existing datasets with off-the-shelf bounding boxes such as ImageNet and Open Images, demonstrating the superiority of our IOG as an annotation tool.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_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-object-segmentation-with-inside","repo_url":"https://github.com/shiyinzhang/Inside-Outside-Guidance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"interactive-object-segmentation-with-inside","repo_url":"https://github.com/shiyinzhang/Pixel-ImageNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"interactive-segmentation","task_name":"Interactive Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dextr","method_name":"DEXTR"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pyramid-pooling-module","method_name":"Pyramid Pooling Module"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/interactive-segmentation-on-coco","task":"Interactive Segmentation","dataset":"COCO (Common Objects in Context)","model":"IOG","rank_in_archive_order":1,"of":1,"metrics":{"Instance Average IoU":"85.2"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-cityscapes-val","task":"Interactive Segmentation","dataset":"Cityscapes val","model":"IOG","rank_in_archive_order":1,"of":1,"metrics":{"Instance Average IoU":"83.8"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-pascal2coco","task":"Interactive Segmentation","dataset":"PASCAL2COCO(Unseen)","model":"IOG","rank_in_archive_order":1,"of":1,"metrics":{"Instance Average IoU":"82.1"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-rooftop","task":"Interactive Segmentation","dataset":"Rooftop","model":"IOG","rank_in_archive_order":1,"of":2,"metrics":{"Instance Average IoU":"94.0"},"uses_additional_data":false},{"leaderboard":"/sota/interactive-segmentation-on-sstem","task":"Interactive Segmentation","dataset":"ssTEM","model":"IOG","rank_in_archive_order":1,"of":1,"metrics":{"Instance Average IoU":"83.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}