{"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/elastic-improving-cnns-with-instance-specific","title":"ELASTIC: Improving CNNs with Dynamic Scaling Policies","arxiv_id":"1812.05262","date":"2018-12-13","proceeding":"CVPR 2019 6","authors":["Huiyu Wang","Aniruddha Kembhavi","Ali Farhadi","Alan Yuille","Mohammad Rastegari"],"abstract":"Scale variation has been a challenge from traditional to modern approaches in\ncomputer vision. Most solutions to scale issues have a similar theme: a set of\nintuitive and manually designed policies that are generic and fixed (e.g. SIFT\nor feature pyramid). We argue that the scaling policy should be learned from\ndata. In this paper, we introduce ELASTIC, a simple, efficient and yet very\neffective approach to learn a dynamic scale policy from data. We formulate the\nscaling policy as a non-linear function inside the network's structure that (a)\nis learned from data, (b) is instance specific, (c) does not add extra\ncomputation, and (d) can be applied on any network architecture. We applied\nELASTIC to several state-of-the-art network architectures and showed consistent\nimprovement without extra (sometimes even lower) computation on ImageNet\nclassification, MSCOCO multi-label classification, and PASCAL VOC semantic\nsegmentation. Our results show major improvement for images with scale\nchallenges. 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