{"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/group-wise-deep-object-co-segmentation-with","title":"Group-Wise Deep Object Co-Segmentation With Co-Attention Recurrent Neural Network","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Bo Li"," Zhengxing Sun"," Qian Li"," Yunjie Wu"," Anqi Hu"],"abstract":"Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for real-world co-segmentation. This paper proposes a novel end-to-end deep learning approach for group-wise object co-segmentation with a recurrent network architecture. Specifically, the semantic features extracted from a pre-trained CNN of each image are first processed by single image representation branch to learn the unique properties. Meanwhile, a specially designed Co-Attention Recurrent Unit (CARU) recurrently explores all images to generate the final group representation by using the co-attention between images, and simultaneously suppresses noisy information. The group feature which contains synergetic information is broadcasted to each individual image and fused with multi-scale fine-resolution features to facilitate the inferring of co-segmentation. Moreover, we propose a groupwise training objective to utilize the co-object similarity and figure-ground distinctness as the additional supervision. The whole modules are collaboratively optimized in an end-to-end manner, further improving the robustness of the approach. Comprehensive experiments on three benchmarks can demonstrate the superiority of our approach in comparison with the state-of-the-art methods.\r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Group-Wise_Deep_Object_Co-Segmentation_With_Co-Attention_Recurrent_Neural_Network_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Group-Wise_Deep_Object_Co-Segmentation_With_Co-Attention_Recurrent_Neural_Network_ICCV_2019_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":"group-wise-deep-object-co-segmentation-with","repo_url":"https://github.com/francesco-p/group-wise-iccv19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}