While deep learning has significantly improved ReID model accuracy under the independent and identical distribution (IID) assumption, it has also become clear that such models degrade notably when applied to an unseen novel domain due to unpredictable/unknown domain shift. Contemporary domain generalization (DG) ReID models struggle in learning domain-invariant representation solely through training on an instance classification objective. We consider that a deep learning model is heavily influenced and therefore biased towards domain-specific characteristics, e.g., background clutter, scale and viewpoint variations, limiting the generalizability of the learned model, and hypothesize that the pedestrians are domain invariant owning they share the same structural characteristics. To enable the ReID model to be less domain-specific from these pure pedestrians, we introduce a method that guides model learning of the primary ReID instance classification objective by a concurrent auxiliary learning objective on weakly labeled pedestrian saliency detection. To solve the problem of conflicting optimization criteria in the model parameter space between the two learning objectives, we introduce a Primary-Auxiliary Objectives Association (PAOA) mechanism to calibrate the loss gradients of the auxiliary task towards the primary learning task gradients. Benefiting from the harmonious multitask learning design, our model can be extended with the recent test-time diagram to form the PAOA+, which performs on-the-fly optimization against the auxiliary objective in order to maximize the model's generative capacity in the test target domain. Experiments demonstrate the superiority of the proposed PAOA model.

翻译：尽管深度学习在独立同分布假设下显著提升了行人重识别模型的精度，但当模型应用于未见过的陌生领域时，由于不可预测/未知的领域偏移，其性能明显退化。现有的领域泛化行人重识别模型仅通过实例分类目标训练来学习领域不变表征，这存在局限性。我们认为深度学习模型受到领域特定特征（如背景杂乱、尺度和视角变化）的强烈影响并产生偏差，限制了所学模型的泛化能力，同时假设行人因共享相同结构特征而具有领域不变性。为使行人重识别模型能更少地依赖这些纯粹行人的领域特定性，我们提出一种方法：通过一个并行辅助学习目标（基于弱标签的行人显著性检测）来引导主实例分类目标的学习。为解决两个学习目标在模型参数空间中存在的优化准则冲突问题，我们引入主辅目标关联机制，将辅助任务的损失梯度校准至主学习任务的梯度方向。得益于和谐的多任务学习设计，我们的模型可扩展至最新测试时优化范式，形成PAOA+，即通过针对辅助目标进行即时优化来最大化模型在测试目标域中的生成能力。实验证明了所提出PAOA模型的优越性。