Adversarial Diffusion Model for Unsupervised Domain-Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2412.16859v1
• Date: Sun, 22 Dec 2024 04:55:41 GMT
• Title: Adversarial Diffusion Model for Unsupervised Domain-Adaptive Semantic Segmentation
• Authors: Jongmin Yu, Zhongtian Sun, Shan Luo,
• Abstract summary: This paper presents a novel method, the Conditional and Inter-coder Connected Latent Diffusion (CICLD) based Semantic Model.<n> CICLD incorporates a conditioning mechanism to improve contextual understanding during segmentation and an inter-coder connection to preserve fine-labelled details and spatial hierarchies.<n>Extensive experiments are conducted across three benchmark datasets-GTA5, Synthia, and Cityscape-shows that CICLD outperforms state-of-the-art UDA methods.
• Score: 8.320092945636032
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation requires labour-intensive labelling tasks to obtain the supervision signals, and because of this issue, it is encouraged that using domain adaptation, which transfers information from the existing labelled source domains to unlabelled or weakly labelled target domains, is essential. However, it is intractable to find a well-generalised representation which can describe two domains due to probabilistic or geometric difference between the two domains. This paper presents a novel method, the Conditional and Inter-coder Connected Latent Diffusion (CICLD) based Semantic Segmentation Model, to advance unsupervised domain adaptation (UDA) for semantic segmentation tasks. Leveraging the strengths of latent diffusion models and adversarial learning, our method effectively bridges the gap between synthetic and real-world imagery. CICLD incorporates a conditioning mechanism to improve contextual understanding during segmentation and an inter-coder connection to preserve fine-grained details and spatial hierarchies. Additionally, adversarial learning aligns latent feature distributions across source, mixed, and target domains, further enhancing generalisation. Extensive experiments are conducted across three benchmark datasets-GTA5, Synthia, and Cityscape-shows that CICLD outperforms state-of-the-art UDA methods. Notably, the proposed method achieves a mean Intersection over Union (mIoU) of 74.4 for the GTA5 to Cityscape UDA setting and 67.2 mIoU for the Synthia to Cityscape UDA setting. This project is publicly available on 'https://github.com/andreYoo/CICLD'.

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