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A Study on Self-Supervised Object Detection Pretraining. (arXiv:2207.04186v2 [cs.CV] UPDATED)
Aug. 12, 2022, 1:12 a.m. | Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin, Maryam Khademi
cs.CV updates on arXiv.org arxiv.org
In this work, we study different approaches to self-supervised pretraining of
object detection models. We first design a general framework to learn a
spatially consistent dense representation from an image, by randomly sampling
and projecting boxes to each augmented view and maximizing the similarity
between corresponding box features. We study existing design choices in the
literature, such as box generation, feature extraction strategies, and using
multiple views inspired by its success on instance-level image representation
learning techniques. Our results suggest …
More from arxiv.org / cs.CV updates on arXiv.org
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