{"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/analysing-domain-shift-factors-between-videos","title":"Analysing domain shift factors between videos and images for object detection","arxiv_id":"1501.01186","date":"2015-01-06","proceeding":null,"authors":["Vicky Kalogeiton","Vittorio Ferrari","Cordelia Schmid"],"abstract":"Object detection is one of the most important challenges in computer vision.\nObject detectors are usually trained on bounding-boxes from still images.\nRecently, video has been used as an alternative source of data. Yet, for a\ngiven test domain (image or video), the performance of the detector depends on\nthe domain it was trained on. In this paper, we examine the reasons behind this\nperformance gap. We define and evaluate different domain shift factors: spatial\nlocation accuracy, appearance diversity, image quality and aspect distribution.\nWe examine the impact of these factors by comparing performance before and\nafter factoring them out. The results show that all four factors affect the\nperformance of the detectors and their combined effect explains nearly the\nwhole performance gap.","url_abs":"http://arxiv.org/abs/1501.01186v3","url_pdf":"http://arxiv.org/pdf/1501.01186v3.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":"analysing-domain-shift-factors-between-videos","repo_url":"https://github.com/vkalogeiton/yto-dataset","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1501.01186","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}