Auto-labeling systems and applications for open-set and out-of-domain segmentation

Abstract

Approaches are disclosed herein for an automatic segmentation labeling system that identifies objects for potential open-class categories and generates segmentation masks for objects. The disclosed system may use a training pipeline that trains two segmentation models. The training pipeline may take, as input, a set of images with bounding boxes and class labels. The set of images may be fed into a first segmentation network with the bounding boxes used as ground truth for weak supervision. The first segmentation network may be trained to generate pseudo segmentation masks. In a second stage, the trained first segmentation network is used to generate pseudo masks for a set of input images. The generated pseudo masks are provided as input, along with the corresponding images, to a second segmentation network to be used as a type of ground truth data for training the second segmentation network to generate high-quality segmentation masks.

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