{"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/trickvos-a-bag-of-tricks-for-video-object","title":"TrickVOS: A Bag of Tricks for Video Object Segmentation","arxiv_id":"2306.15377","date":"2023-06-27","proceeding":null,"authors":["Evangelos Skartados","Konstantinos Georgiadis","Mehmet Kerim Yucel","Koskinas Ioannis","Armando Domi","Anastasios Drosou","Bruno Manganelli","Albert Saa-Garriga"],"abstract":"Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance. In this work, we identify three key aspects where we can improve such methods; i) supervisory signal, ii) pretraining and iii) spatial awareness. We then propose TrickVOS; a generic, method-agnostic bag of tricks addressing each aspect with i) a structure-aware hybrid loss, ii) a simple decoder pretraining regime and iii) a cheap tracker that imposes spatial constraints in model predictions. Finally, we propose a lightweight network and show that when trained with TrickVOS, it achieves competitive results to state-of-the-art methods on DAVIS and YouTube benchmarks, while being one of the first STM-based SVOS methods that can run in real-time on a mobile device.","url_abs":"https://arxiv.org/abs/2306.15377v2","url_pdf":"https://arxiv.org/pdf/2306.15377v2.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":[],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"object","task_name":"Object"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-video-object-segmentation","task_name":"Semi-Supervised Video Object Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-davis-2016","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS 2016","model":"STCN + TrickVOS (PT)","rank_in_archive_order":16,"of":78,"metrics":{"F-measure (Mean)":"93.1","J&F":"91.8","Jaccard (Mean)":"90.5"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-davis-2016","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS 2016","model":"Lightweight TrickVOS (PT)","rank_in_archive_order":36,"of":78,"metrics":{"F-measure (Mean)":"89.9","J&F":"89.3","Jaccard (Mean)":"88.7","Speed (FPS)":"86.4"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-video-object-segmentation-on-3","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS-2016","model":"STCN + TrickVOS (PT)","rank_in_archive_order":1,"of":1,"metrics":{"Speed (FPS)":"45.4"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-video-object-segmentation-on-2","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS-2017","model":"STCN + TrickVOS (PT)","rank_in_archive_order":1,"of":2,"metrics":{"F-measure (Mean)":"89.6","J&F":"86.1","Jaccard (Mean)":"82.6","Speed (FPS)":"35.1"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-video-object-segmentation-on-2","task":"Semi-Supervised Video Object Segmentation","dataset":"DAVIS-2017","model":"Lightweight TrickVOS (PT)","rank_in_archive_order":2,"of":2,"metrics":{"F-measure (Mean)":"86","J&F":"82.7","Jaccard (Mean)":"79.4","Speed (FPS)":"76.4"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-video-object-segmentation-on-18","task":"Semi-Supervised Video Object Segmentation","dataset":"YouTube-VOS 2019","model":"STCN + TrickVOS (PT)","rank_in_archive_order":21,"of":22,"metrics":{"F-Measure (Seen)":"86.4","F-Measure (Unseen)":"85.5","J&F":"82.8","Jaccard (Seen)":"82.1","Jaccard (Unseen)":"77.2"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-video-object-segmentation-on-18","task":"Semi-Supervised Video Object Segmentation","dataset":"YouTube-VOS 2019","model":"Lightweight TrickVOS (PT)","rank_in_archive_order":22,"of":22,"metrics":{"F-Measure (Seen)":"83.3","F-Measure (Unseen)":"84","J score (unseen)":"75.2","J&F":"80.5","Jaccard (Seen)":"79.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}