  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.
