MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2103.12991v1
• Date: Wed, 24 Mar 2021 05:13:23 GMT
• Title: MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation
• Authors: Jiaxing Huang, Dayan Guan, Shijian Lu, Aoran Xiao
• Abstract summary: This paper presents a novel multi-level adversarial network (MLAN) that aims to address inter-domain inconsistency at both global image level and local region level optimally. MLAN has two novel designs, namely, region-level adversarial learning (RL-AL) and co-regularized adversarial learning (CR-AL) Extensive experiments show that MLAN outperforms the state-of-the-art with a large margin consistently across multiple datasets.
• Score: 32.77436219094282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation. However, most adversarial learning based methods align source and target distributions at a global image level but neglect the inconsistency around local image regions. This paper presents a novel multi-level adversarial network (MLAN) that aims to address inter-domain inconsistency at both global image level and local region level optimally. MLAN has two novel designs, namely, region-level adversarial learning (RL-AL) and co-regularized adversarial learning (CR-AL). Specifically, RL-AL models prototypical regional context-relations explicitly in the feature space of a labelled source domain and transfers them to an unlabelled target domain via adversarial learning. CR-AL fuses region-level AL and image-level AL optimally via mutual regularization. In addition, we design a multi-level consistency map that can guide domain adaptation in both input space ($i.e.$, image-to-image translation) and output space ($i.e.$, self-training) effectively. Extensive experiments show that MLAN outperforms the state-of-the-art with a large margin consistently across multiple datasets.

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