  Person Re-IDentification (ReID) aims at re-identifying persons from different
viewpoints across multiple cameras. Capturing the fine-grained appearance
differences is often the key to accurate person ReID, because many identities
can be differentiated only when looking into these fine-grained differences.
However, most state-of-the-art person ReID approaches, typically driven by a
triplet loss, fail to effectively learn the fine-grained features as they are
focused more on differentiating large appearance differences. To address this
issue, we introduce a novel pairwise loss function that enables ReID models to
learn the fine-grained features by adaptively enforcing an exponential
penalization on the images of small differences and a bounded penalization on
the images of large differences. The proposed loss is generic and can be used
as a plugin to replace the triplet loss to significantly enhance different
types of state-of-the-art approaches. Experimental results on four benchmark
datasets show that the proposed loss substantially outperforms a number of
popular loss functions by large margins; and it also enables significantly
improved data efficiency.
