June 5, 2024, 4:49 a.m. | Mehmet Ayg\"un, Prithviraj Dhar, Zhicheng Yan, Oisin Mac Aodha, Rakesh Ranjan

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.02535v1 Announce Type: new
Abstract: Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which can be expensive to obtain. Self-supervised learning attempts to circumvent the requirement for labeled data by learning representations from raw unlabeled visual data alone. However, unlike humans who obtain rich 3D information from their binocular vision and through motion, the majority of current self-supervised methods are tasked with learning …

abstract arxiv computer computer vision cs.cv data fundamental prior raw representation representation learning robust self-supervised learning supervised learning training training models type vision visual visual data

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