用于无监督语义分割的对抗式域自适应潜在扩散
Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation
AI总结：
针对虚拟与真实域差异导致语义分割泛化难的问题，提出基于潜在扩散的ICCLD方法，通过编码器间连接和对抗学习对齐特征，在多个数据集上超越现有UDA方法。
摘要：
语义分割需要大量像素级标注，促使无监督域适应（UDA）将知识从有标签源域迁移到无标签或弱标签目标域。最高效的策略之一是使用在受控虚拟环境（如视频游戏或交通模拟器）中生成的合成数据集，这些环境能自动生成像素级标注。然而，即使有此类数据集，由于虚拟世界与真实世界图像间的概率和几何差异，学习能同时捕捉两个域的泛化良好的表示仍具挑战。本工作引入一种基于潜在扩散模型的语义分割方法，称为Inter-Coder Connected Latent Diffusion（ICCLD），并提出一种无监督域适应方法。模型采用编码器间连接增强上下文理解并保留精细细节，同时对抗学习在潜在扩散过程中对齐跨域潜在特征分布。在GTA5、Synthia和Cityscapes上的实验表明，ICCLD优于最先进的UDA方法，实现mIoU分数74.4（GTA5→Cityscapes）和67.2（Synthia→Cityscapes）。
英文摘要：
Semantic segmentation requires extensive pixel-level annotation, motivating unsupervised domain adaptation (UDA) to transfer knowledge from labelled source domains to unlabelled or weakly labelled target domains. One of the most efficient strategies involves using synthetic datasets generated within controlled virtual environments, such as video games or traffic simulators, which can automatically generate pixel-level annotations. However, even when such datasets are available, learning a well-generalised representation that captures both domains remains challenging, owing to probabilistic and geometric discrepancies between the virtual world and real-world imagery. This work introduces a semantic segmentation method based on latent diffusion models, termed Inter-Coder Connected Latent Diffusion (ICCLD), alongside an unsupervised domain adaptation approach. The model employs an inter-coder connection to enhance contextual understanding and preserve fine details, while adversarial learning aligns latent feature distributions across domains during the latent diffusion process. Experiments on GTA5, Synthia, and Cityscapes demonstrate that ICCLD outperforms state-of-the-art UDA methods, achieving mIoU scores of 74.4 (GTA5$\rightarrow$Cityscapes) and 67.2 (Synthia$\rightarrow$Cityscapes).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
