Abstract
Object appearances often 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(re-id). In this paper, we suggest these dramatic appearance changes are indications that an object ID is composed of multiple natural groups and it is counter-productive 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 triplets will respect the natural sub-groupings within an object ID. We use this triplet mining mechanism to establish a pose-aware, well-conditioned triplet cost function. This allows a single network to be trained with fixed parameters across three challenging benchmarks, while still providing state-of-the-art re-identification results.
Abstract (translated)
URL
https://arxiv.org/abs/2110.07933
