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Object-Aware Cropping for Self-Supervised Learning
[article]
2021
arXiv
pre-print
A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-supervised loss. The underlying assumption is that randomly cropped and resized regions of a given image share information about the objects of interest, which the learned representation will capture. This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly
arXiv:2112.00319v1
fatcat:uaj7oexio5flde66ubcq5ybxmi