  Person re-identification (re-id), which aims to retrieve images of the same
person in a given image from a database, is one of the most practical image
recognition applications. In the real world, however, the environments that the
images are taken from change over time. This causes a distribution shift
between training and testing and degrades the performance of re-id. To maintain
re-id performance, models should continue adapting to the test environment's
temporal changes. Test-time adaptation (TTA), which aims to adapt models to the
test environment with only unlabeled test data, is a promising way to handle
this problem because TTA can adapt models instantly in the test environment.
However, the previous TTA methods are designed for classification and cannot be
directly applied to re-id. This is because the set of people's identities in
the dataset differs between training and testing in re-id, whereas the set of
classes is fixed in the current TTA methods designed for classification. To
improve re-id performance in changing test environments, we propose TEst-time
similarity Modification for Person re-identification (TEMP), a novel TTA method
for re-id. TEMP is the first fully TTA method for re-id, which does not require
any modification to pre-training. Inspired by TTA methods that refine the
prediction uncertainty in classification, we aim to refine the uncertainty in
re-id. However, the uncertainty cannot be computed in the same way as
classification in re-id since it is an open-set task, which does not share
person labels between training and testing. Hence, we propose re-id entropy, an
alternative uncertainty measure for re-id computed based on the similarity
between the feature vectors. Experiments show that the re-id entropy can
measure the uncertainty on re-id and TEMP improves the performance of re-id in
online settings where the distribution changes over time.
