  Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a
challenging task: to avoid a costly annotation of additional data, it aims at
transferring knowledge from a domain with annotated data to a domain of
interest with only unlabeled data. Pseudo-labeling approaches have proven to be
effective for UDA re-ID. However, the effectiveness of these approaches heavily
depends on the choice of some hyperparameters (HP) that affect the generation
of pseudo-labels by clustering. The lack of annotation in the domain of
interest makes this choice non-trivial. Current approaches simply reuse the
same empirical value for all adaptation tasks and regardless of the target data
representation that changes through pseudo-labeling training phases. As this
simplistic choice may limit their performance, we aim at addressing this issue.
We propose new theoretical grounds on HP selection for clustering UDA re-ID as
well as method of automatic and cyclic HP tuning for pseudo-labeling UDA
clustering: HyPASS. HyPASS consists in incorporating two modules in
pseudo-labeling methods: (i) HP selection based on a labeled source validation
set and (ii) conditional domain alignment of feature discriminativeness to
improve HP selection based on source samples. Experiments on commonly used
person re-ID and vehicle re-ID datasets show that our proposed HyPASS
consistently improves the best state-of-the-art methods in re-ID compared to
the commonly used empirical HP setting.
