{"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/self-supervised-learning-via-conditional","title":"Self-Supervised Learning via Conditional Motion Propagation","arxiv_id":"1903.11412","date":"2019-03-27","proceeding":"CVPR 2019 6","authors":["Xiaohang Zhan","Xingang Pan","Ziwei Liu","Dahua Lin","Chen Change Loy"],"abstract":"Intelligent agent naturally learns from motion. Various self-supervised\nalgorithms have leveraged motion cues to learn effective visual\nrepresentations. The hurdle here is that motion is both ambiguous and complex,\nrendering previous works either suffer from degraded learning efficacy, or\nresort to strong assumptions on object motions. In this work, we design a new\nlearning-from-motion paradigm to bridge these gaps. Instead of explicitly\nmodeling the motion probabilities, we design the pretext task as a conditional\nmotion propagation problem. Given an input image and several sparse flow\nguidance vectors on it, our framework seeks to recover the full-image motion.\nCompared to other alternatives, our framework has several appealing properties:\n(1) Using sparse flow guidance during training resolves the inherent motion\nambiguity, and thus easing feature learning. (2) Solving the pretext task of\nconditional motion propagation encourages the emergence of kinematically-sound\nrepresentations that poss greater expressive power. Extensive experiments\ndemonstrate that our framework learns structural and coherent features; and\nachieves state-of-the-art self-supervision performance on several downstream\ntasks including semantic segmentation, instance segmentation, and human\nparsing. Furthermore, our framework is successfully extended to several useful\napplications such as semi-automatic pixel-level annotation. Project page:\n\"http://mmlab.ie.cuhk.edu.hk/projects/CMP/\".","url_abs":"http://arxiv.org/abs/1903.11412v3","url_pdf":"http://arxiv.org/pdf/1903.11412v3.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":"self-supervised-learning-via-conditional","repo_url":"https://github.com/XiaohangZhan/conditional-motion-propagation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"human-parsing","task_name":"Human Parsing"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.11412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.11412"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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