{"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/motion-attentive-transition-for-zero-shot","title":"Motion-Attentive Transition for Zero-Shot Video Object Segmentation","arxiv_id":"2003.04253","date":"2020-03-09","proceeding":null,"authors":["Tianfei Zhou","Shunzhou Wang","Yi Zhou","Yazhou Yao","Jianwu Li","Ling Shao"],"abstract":"In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e. DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts.","url_abs":"https://arxiv.org/abs/2003.04253v3","url_pdf":"https://arxiv.org/pdf/2003.04253v3.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":"motion-attentive-transition-for-zero-shot","repo_url":"https://github.com/tfzhou/MATNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"object","task_name":"Object"},{"task_slug":"segmentation","task_name":"Segmentation"},{"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":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-10","task":"Unsupervised Video Object Segmentation","dataset":"DAVIS 2016 val","model":"MATNet","rank_in_archive_order":19,"of":25,"metrics":{"F":"80.7","G":"81.6","J":"82.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-11","task":"Unsupervised Video Object Segmentation","dataset":"FBMS test","model":"MATNet","rank_in_archive_order":13,"of":15,"metrics":{"J":"76.1"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-12","task":"Unsupervised Video Object Segmentation","dataset":"YouTube-Objects","model":"MATNet","rank_in_archive_order":15,"of":16,"metrics":{"J":"69.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.04253","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}