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Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

2022-05-31 13:30:36
Jose L. Gómez, Gabriel Villalonga, Antonio M. López

Abstract

Semantic image segmentation is addressed by training deep models. Since supervised training draws to a curse of human-based image labeling, using synthetic images with automatically generated ground truth together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we proposed a new co-training process for synth-to-real UDA of semantic segmentation models. First, we design a self-training procedure which provides two initial models. Then, we keep training these models in a collaborative manner for obtaining the final model. The overall process treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, {\ie}, neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets. Our co-training shows improvements of 15-20 percentage points of mIoU over baselines, so establishing new state-of-the-art results.

Abstract (translated)

URL

https://arxiv.org/abs/2205.15781

PDF

https://arxiv.org/pdf/2205.15781.pdf


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