用于领域自适应图像分割的对偶形式互补掩码
Dual form Complementary Masking for Domain-Adaptive Image Segmentation
AI总结：
本文将掩码重建重构为稀疏信号重建问题，证明对偶互补掩码能提取领域无关特征，并提出MaskTwins框架将其集成到主训练流程中，通过互补掩码预测一致性实现端到端领域泛化，在自然和生物图像分割中优于基线方法。
摘要：
近期的研究将掩码图像建模（MIM）与无监督领域自适应（UDA）中的一致性正则化关联起来。然而，它们仅将掩码视为输入图像上的一种特殊变形形式，忽略了理论分析，导致对掩码重建的理解较为肤浅，且未能充分挖掘其在增强特征提取和表示学习方面的潜力。本文将掩码重建重新构建为稀疏信号重建问题，并从理论上证明了对偶形式的互补掩码在提取领域无关图像特征方面具有卓越能力。基于这一深刻见解，我们提出MaskTwins，一个简单而有效的UDA框架，将掩码重建直接集成到主训练流程中。MaskTwins通过强制互补掩码图像预测之间的一致性，揭示跨不同领域持续存在的内在结构模式，以端到端方式实现领域泛化。大量实验验证了MaskTwins在自然和生物图像分割中优于基线方法。这些结果证明MaskTwins在无需单独预训练的情况下提取领域不变特征的显著优势，为领域自适应分割提供新范式。
英文摘要：
Recent works have correlated Masked Image Modeling (MIM) with consistency regularization in Unsupervised Domain Adaptation (UDA). However, they merely treat masking as a special form of deformation on the input images and neglect the theoretical analysis, which leads to a superficial understanding of masked reconstruction and insufficient exploitation of its potential in enhancing feature extraction and representation learning. In this paper, we reframe masked reconstruction as a sparse signal reconstruction problem and theoretically prove that the dual form of complementary masks possesses superior capabilities in extracting domain-agnostic image features. Based on this compelling insight, we propose MaskTwins, a simple yet effective UDA framework that integrates masked reconstruction directly into the main training pipeline. MaskTwins uncovers intrinsic structural patterns that persist across disparate domains by enforcing consistency between predictions of images masked in complementary ways, enabling domain generalization in an end-to-end manner. Extensive experiments verify the superiority of MaskTwins over baseline methods in natural and biological image segmentation. These results demonstrate the significant advantages of MaskTwins in extracting domain-invariant features without the need for separate pre-training, offering a new paradigm for domain-adaptive segmentation.
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。
