  Many real-world applications, such as city-scale traffic monitoring and
control, requires large-scale re-identification. However, previous ReID methods
often failed to address two limitations in existing ReID benchmarks, i.e., low
spatiotemporal coverage and sample imbalance. Notwithstanding their
demonstrated success in every single benchmark, they have difficulties in
generalizing to unseen environments. As a result, these methods are less
applicable in a large-scale setting due to poor generalization. In seek for a
highly generalizable large-scale ReID method, we present an adversarial domain
invariant feature learning framework (ADIN) that explicitly learns to separate
identity-related features from challenging variations, where for the first time
"free" annotations in ReID data such as video timestamp and camera index are
utilized. Furthermore, we find that the imbalance of nuisance classes
jeopardizes the adversarial training, and for mitigation we propose a
calibrated adversarial loss that is attentive to nuisance distribution.
Experiments on existing large-scale person vehicle ReID datasets demonstrate
that ADIN learns more robust and generalizable representations, as evidenced by
its outstanding direct transfer performance across datasets, which is a
criterion that can better measure the generalizability of large-scale ReID
methods/
