META-LEARNED FEATURE CRITICS FOR DOMAIN GENERALIZED SEMANTIC SEGMENTATION
Journal
Proceedings - International Conference on Image Processing, ICIP
Journal Volume
2021-September
Pages
2244-2248
Date Issued
2021
Author(s)
Abstract
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. In this paper, we address domain generalized semantic segmentation, in which the segmentation model is trained on multiple source domains and is expected to generalize to unseen data domains. We propose a novel meta-learning scheme with feature disentanglement ability, which derives domain-invariant features for semantic segmentation with domain generalization guarantees. In particular, we introduce a class-specific feature critic module in our framework, enforcing the disentangled visual features with domain generalization guarantees. Finally, our quantitative results on benchmark datasets confirm the effectiveness and robustness of our proposed model, performing favorably against state-of-the-art domain adaptation and generalization methods in segmentation. © 2021 IEEE.
Subjects
Computer vision; Deep learning; Domain generalization; Meta-learning; Semantic segmentation
Type
conference paper