{"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/assessing-domain-gap-for-continual-domain","title":"Assessing Domain Gap for Continual Domain Adaptation in Object Detection","arxiv_id":"2302.10396","date":"2023-02-21","proceeding":null,"authors":["Anh-Dzung Doan","Bach Long Nguyen","Surabhi Gupta","Ian Reid","Markus Wagner","Tat-Jun Chin"],"abstract":"To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather, and seasons. 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