Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

• URL: http://arxiv.org/abs/2205.15781v1
• Date: Tue, 31 May 2022 13:30:36 GMT
• Title: Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
• Authors: Jose L. G\'omez, Gabriel Villalonga and Antonio M. L\'opez
• Abstract summary: We propose a new co-training process for synth-to-real UDA of semantic segmentation models. Our co-training shows improvements of 15-20 percentage points of mIoU over baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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