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RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training
[article]
2020
arXiv
pre-print
Although well-known large-scale datasets, such as ImageNet, have driven image understanding forward, most of these datasets require extensive manual annotation and are thus not easily scalable. This limits the advancement of image understanding techniques. The impact of these large-scale datasets can be observed in almost every vision task and technique in the form of pre-training for initialization. In this work, we propose an easily scalable and self-supervised technique that can be used to
arXiv:2002.02200v1
fatcat:ovydmbvdargafe7mo7jx7p3rou