Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://github.com/Astaxanthin/AdaSP.

翻译：目标重识别（ReID）旨在从大规模图库中寻找与给定查询具有相同身份的目标实例。成对损失在训练强健的重识别网络中扮演着重要角色。现有成对损失密集地利用每个实例作为锚点，并在小批量中采样其三元组。这种密集采样机制不可避免地会引入视觉相似性较少的正样本对，从而对训练产生不利影响。为解决该问题，我们提出了一种称为稀疏成对（SP）损失的新型损失范式，该范式仅在小批量中为每个类别利用少量合适的样本对，并经验性地证明了其对重识别任务而言是充分的。基于所提出的损失框架，我们进一步提出一种自适应正样本挖掘策略，该策略能够动态适应多样的类内变化。大量实验表明，SP损失及其自适应变体AdaSP损失优于其他成对损失，并在多个重识别基准上取得了最优性能。代码可在 https://github.com/Astaxanthin/AdaSP 获取。