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. CICLD incorporates a conditioning mechanism to improve contextual understanding during segmentation and an inter-coder connection to preserve fine-labelled details and spatial hierarchies. 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:
• 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'.

Related papers

• Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation [30.606689882397223]
  Unsupervised domain adaptation (UDA) techniques are vital for semantic segmentation in geosciences. Most existing UDA methods, which focus on domain alignment at the high-level feature space, struggle to simultaneously retain local spatial details and global contextual semantics. A novel decomposition scheme is proposed to guide domain-invariant representation learning.
  arXiv  Detail & Related papers  (2024-04-06T07:13:49Z)
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• Unified Domain Adaptive Semantic Segmentation [96.74199626935294]
  Unsupervised Adaptive Domain Semantic (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. We propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies. Our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T09:18:49Z)
• Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation [14.65316832227658]
  Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. One prevailing strategy is to minimize the distribution discrepancy by aligning their semantic features extracted by deep models.
  arXiv  Detail & Related papers  (2022-02-27T08:36:12Z)
• Semi-supervised Domain Adaptation for Semantic Segmentation [3.946367634483361]
  We propose a novel two-step semi-supervised dual-domain adaptation (SSDDA) approach to address both cross- and intra-domain gaps in semantic segmentation. We demonstrate that the proposed approach outperforms state-of-the-art methods on two common synthetic-to-real semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-20T16:13:00Z)
• Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer [27.64947077788111]
  Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data adapt to real images. Previous feature-level adversarial learning methods only consider adapting models on the high-level semantic features. We present the first attempt at explicitly using low-level edge information, which has a small inter-domain gap, to guide the transfer of semantic information.
  arXiv  Detail & Related papers  (2021-09-18T11:51:31Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation [44.19436340246248]
  This paper presents an innovative local contextual-relation consistent domain adaptation technique. It aims to achieve local-level consistencies during the global-level alignment. Experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-05T19:00:46Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)
• MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation [58.38749495295393]
  Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Recent multi-source domain adaptation (MDA) methods do not consider the pixel-level alignment between sources and target. We propose a novel MDA framework to address these challenges.
  arXiv  Detail & Related papers  (2020-02-19T21:22:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.