标题： PCaM：一种基于渐进焦点注意力的信息融合方法，用于提高视觉变压器领域适应性 显示英文标题

标题： PCaM: A Progressive Focus Attention-Based Information Fusion Method for Improving Vision Transformer Domain Adaptation

摘要： 无监督域适应（UDA）旨在将标记的源域知识转移到未标记的目标域。 基于视觉变压器（ViTs）的最新UDA方法通过基于注意力的特征对齐实现了强大的性能。 然而，我们发现一个关键限制：前景物体不匹配，即不同域之间前景物体大小和空间分布的差异会削弱注意力一致性并阻碍有效的域对齐。 为了解决这个问题，我们提出了渐进式焦点交叉注意力机制（PCaM），该机制在交叉注意力过程中逐步过滤背景信息，使模型能够专注于并融合跨域的判别性前景语义。 我们进一步引入了一个注意力引导损失，该损失明确引导注意力到任务相关区域，增强跨域注意力一致性。 PCaM轻量、与架构无关，并且易于集成到现有的ViT-based UDA流程中。 在Office-Home、DomainNet、VisDA-2017和遥感数据集上的大量实验表明，PCaM显著提高了适应性能，并达到了新的最先进结果，验证了注意力引导的前景融合在域适应中的有效性。 显示英文摘要

摘要： Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent UDA methods based on Vision Transformers (ViTs) have achieved strong performance through attention-based feature alignment. However, we identify a key limitation: foreground object mismatch, where the discrepancy in foreground object size and spatial distribution across domains weakens attention consistency and hampers effective domain alignment. To address this issue, we propose the Progressive Focus Cross-Attention Mechanism (PCaM), which progressively filters out background information during cross-attention, allowing the model to focus on and fuse discriminative foreground semantics across domains. We further introduce an attentional guidance loss that explicitly directs attention toward task-relevant regions, enhancing cross-domain attention consistency. PCaM is lightweight, architecture-agnostic, and easy to integrate into existing ViT-based UDA pipelines. Extensive experiments on Office-Home, DomainNet, VisDA-2017, and remote sensing datasets demonstrate that PCaM significantly improves adaptation performance and achieves new state-of-the-art results, validating the effectiveness of attention-guided foreground fusion for domain adaptation.