Three-stage semi-supervised instance segmentation training method and system

Abstract

A three-stage semi-supervised instance segmentation training method and a system. In a first stage, a teacher model and a student model are trained based on labeled data. In a second stage, the teacher model performs prediction on unlabeled data and generates pseudo labels according to a prediction result. The student model learns labeled data and unlabeled data based on the pseudo labels. A soft label filter is used to filter for obtaining high-quality pseudo labels, a positive sample loss function is used to eliminate a problem of incorrect model convergence due to the pseudo labels with incomplete information, and the parameters of the student model are updated via a backward propagation process. In a third stage, the parameters of the student model are transferred to the teacher model through an exponential moving average operation, so that the teacher model and the student model are under a same architecture.

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