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Progressive One-shot Human Parsing
[article]
2021
arXiv
pre-print
Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in fashion analysis. In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set of reference classes defined by any single reference example. During training, only base classes defined in the training set are exposed, which can overlap with part of reference
arXiv:2012.11810v3
fatcat:c3nuadbx4jgsli6bsmcznnbpwa