Handling new target classes in semantic segmentation with domain adaptation

• URL: http://arxiv.org/abs/2004.01130v2
• Date: Tue, 16 Feb 2021 13:08:20 GMT
• Title: Handling new target classes in semantic segmentation with domain adaptation
• Authors: Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick P\'erez
• Abstract summary: We propose a framework to enable "boundless" adaptation in the target domain. It relies on a novel architecture, along with a dedicated learning scheme, to bridge the source-target domain gap. Our framework outperforms the baselines by significant margins.
• Score: 34.11498666008825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w.r.t. the source domain, but also includes novel classes that do not exist in the latter. Different to "open-set" and "universal domain adaptation", which both regard all objects from new classes as "unknown", we aim at explicit test-time prediction for these new classes. To reach this goal, we propose a framework that leverages domain adaptation and zero-shot learning techniques to enable "boundless" adaptation in the target domain. It relies on a novel architecture, along with a dedicated learning scheme, to bridge the source-target domain gap while learning how to map new classes' labels to relevant visual representations. The performance is further improved using self-training on target-domain pseudo-labels. For validation, we consider different domain adaptation set-ups, namely synthetic-2-real, country-2-country and dataset-2-dataset. Our framework outperforms the baselines by significant margins, setting competitive standards on all benchmarks for the new task. Code and models are available at https://github.com/valeoai/buda.

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