Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS.

翻译：分布偏移下的泛化一直是计算机视觉领域的重大挑战。在领域泛化（DG）中，直接采用独热标签作为训练目标的常规做法会导致梯度冲突，难以捕捉类别本质特征并增加类内变异。此外，现有DG方法大多忽视源域（可见域）的不同贡献，导致对这些域的学习不均衡。为了解决这些问题，我们首先对DG中梯度冲突的存在进行了理论和实证分析，揭示了优化过程中分布偏移与梯度冲突之间此前未被探索的关系。本文从经验源域风险角度提出了DG的新视角，并设计了一种名为“多样化目标与贡献调度”（DTCS）的新范式。DTCS包含两个创新模块：多样化目标监督（DTS）和多样化贡献平衡（DCB），旨在克服DG中普遍使用的独热标签和源域等贡献方法的局限性。具体而言，DTS采用不同的软标签作为训练目标来应对跨域的不同特征分布，从而缓解梯度冲突；DCB通过确保不同源域损失的公平下降，动态平衡各源域的贡献。在四个基准数据集上的大量实验与分析表明，所提方法在与最先进方法的比较中达到了竞争性性能，验证了DTCS的有效性与优势。