{"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/semantic-video-segmentation-exploring","title":"Semantic Video Segmentation : Exploring Inference Efficiency","arxiv_id":"1509.02441","date":"2015-09-04","proceeding":null,"authors":["Subarna Tripathi","Serge Belongie","Youngbae Hwang","Truong Nguyen"],"abstract":"We explore the efficiency of the CRF inference beyond image level semantic\nsegmentation and perform joint inference in video frames. The key idea is to\ncombine best of two worlds: semantic co-labeling and more expressive models.\nOur formulation enables us to perform inference over ten thousand images within\nseconds and makes the system amenable to perform video semantic segmentation\nmost effectively. On CamVid dataset, with TextonBoost unaries, our proposed\nmethod achieves up to 8% improvement in accuracy over individual semantic image\nsegmentation without additional time overhead. The source code is available at\nhttps://github.com/subtri/video_inference","url_abs":"http://arxiv.org/abs/1509.02441v1","url_pdf":"http://arxiv.org/pdf/1509.02441v1.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":"semantic-video-segmentation-exploring","repo_url":"https://github.com/subtri/video_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"video-segmentation","task_name":"Video Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}