Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2308.12350v1
• Date: Wed, 23 Aug 2023 18:01:01 GMT
• Title: Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation
• Authors: Duo Peng, Ping Hu, Qiuhong Ke, Jun Liu
• Abstract summary: Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS) Existing methods still struggle to preserve semantically-consistent local details between the original and translated images. We present an innovative approach that addresses this challenge by using source-domain labels as explicit guidance during image translation.
• Score: 35.44771460784343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS). However, existing methods still struggle to preserve semantically-consistent local details between the original and translated images. In this work, we present an innovative approach that addresses this challenge by using source-domain labels as explicit guidance during image translation. Concretely, we formulate cross-domain image translation as a denoising diffusion process and utilize a novel Semantic Gradient Guidance (SGG) method to constrain the translation process, conditioning it on the pixel-wise source labels. Additionally, a Progressive Translation Learning (PTL) strategy is devised to enable the SGG method to work reliably across domains with large gaps. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods.

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