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Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging
[article]
2022
arXiv
pre-print
We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account both object and background information to ensure object-background discriminability, rather than using only object appearance. The discriminative learning loss comprises
arXiv:2204.10846v1
fatcat:ztskcslx6vg4bme3n6ja4li2xu