{"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/learning-motion-appearance-co-attention-for","title":"Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Shu Yang","Lu Zhang","Jinqing Qi","Huchuan Lu","Shuo Wang","Xiaoxing Zhang"],"abstract":"    How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive Multi-Modality Collaboration Network (AMC-Net) to utilize appearance and motion information uniformly. Specifically, AMC-Net fuses robust information from multi-modality features and promotes their collaboration in two stages. First, we propose a Multi-Modality Co-Attention Gate (MCG) on the bilateral encoder branches, in which a gate function is used to formulate co-attention scores for balancing the contributions of multi-modality features and suppressing the redundant and misleading information. Then, we propose a Motion Correction Module (MCM) with a visual-motion attention mechanism, which is constructed to emphasize the features of foreground objects by incorporating the spatio-temporal correspondence between appearance and motion cues. Extensive experiments on three public challenging benchmark datasets verify that our proposed network performs favorably against existing state-of-the-art methods via training with fewer data.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_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":"learning-motion-appearance-co-attention-for","repo_url":"https://github.com/isyangshu/amc-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-video-object-segmentation","task_name":"Unsupervised Video Object Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"},{"task_slug":null,"task_name":"Zero-Shot Video Object Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-10","task":"Unsupervised Video Object Segmentation","dataset":"DAVIS 2016 val","model":"AMC-Net","rank_in_archive_order":14,"of":25,"metrics":{"F":"84.6","G":"84.6","J":"84.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-11","task":"Unsupervised Video Object Segmentation","dataset":"FBMS test","model":"AMC-Net","rank_in_archive_order":12,"of":15,"metrics":{"J":"76.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-12","task":"Unsupervised Video Object Segmentation","dataset":"YouTube-Objects","model":"AMC-Net","rank_in_archive_order":8,"of":16,"metrics":{"J":"71.1"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}