  Object appearances change dramatically with pose variations. This creates a
challenge for embedding schemes that seek to map instances with the same object
ID to locations that are as close as possible. This issue becomes significantly
heightened in complex computer vision tasks such as re-identification(reID). In
this paper, we suggest that these dramatic appearance changes are indications
that an object ID is composed of multiple natural groups, and it is
counterproductive to forcefully map instances from different groups to a common
location. This leads us to introduce Relation Preserving Triplet Mining (RPTM),
a feature-matching guided triplet mining scheme, that ensures that triplets
will respect the natural subgroupings within an object ID. We use this triplet
mining mechanism to establish a pose-aware, well-conditioned triplet loss by
implicitly enforcing view consistency. This allows a single network to be
trained with fixed parameters across datasets while providing state-of-the-art
results. Code is available at https://github.com/adhirajghosh/RPTM_reid.
