提出新方法解决无标签域适应难题，提升模型对未知场景的泛化能力。
AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation Model for Open Compound Domain Adaptation
- 基于傅里叶幅值排序构建渐进式学习策略，引导模型从近源域向远源域迁移。
- 引入霍普菲尔德分割模型，将任意域特征映射到源域分布，实现跨域一致性。
- 在两个基准和扩展开放域上达最优，适合动态变化的现实场景应用。
开放复合域适应（OCDA）是一个实际的域适应问题，包含源域、目标复合域和未见开放域。由于复合域和开放域均缺乏域标签和像素级分割标签，现有域适应与泛化方法难以直接应用。为此，我们提出基于幅度的课程学习与霍普菲尔德分割模型用于开放复合域适应（AH-OCDA）。该方法由两个互补组件构成：1）基于幅度的课程学习；2）霍普菲尔德分割模型。在不依赖目标域先验知识的前提下，基于快速傅里叶变换（FFT）对未标注复合域图像进行幅度排序，逐步引导语义分割模型从近源复合域向远源复合域适应。同时，霍普菲尔德分割模型将任意域的分割特征分布映射至源域特征分布。AH-OCDA在两个OCDA基准和扩展开放域上达到当前最优性能，证明其对持续变化的复合域及未见开放域具有强适应能力。
原文摘要 · Abstract (English)
Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, the absence of domain labels and pixel-level segmentation labels for both compound and open domains poses challenges to the direct application of existing domain adaptation and generalization methods. To address this issue, we propose Amplitude-based curriculum learning and a Hopfield segmentation model for Open Compound Domain Adaptation (AH-OCDA). Our method comprises two complementary components: 1) amplitude-based curriculum learning and 2) Hopfield segmentation model. Without prior knowledge of target domains within the compound domains, amplitude-based curriculum learning gradually induces the semantic segmentation model to adapt from the near-source compound domain to the far-source compound domain by ranking unlabeled compound domain images through Fast Fourier Transform (FFT). Additionally, the Hopfield segmentation model maps segmentation feature distributions from arbitrary domains to the feature distributions of the source domain. AH-OCDA achieves state-of-the-art performance on two OCDA benchmarks and extended open domains, demonstrating its adaptability to continuously changing compound domains and unseen open domains.
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