  Human intelligence can retrieve any person according to both visual and
language descriptions. However, the current computer vision community studies
specific person re-identification (ReID) tasks in different scenarios
separately, which limits the applications in the real world. This paper strives
to resolve this problem by proposing a novel instruct-ReID task that requires
the model to retrieve images according to the given image or language
instructions. Instruct-ReID is the first exploration of a general ReID setting,
where existing 6 ReID tasks can be viewed as special cases by assigning
different instructions. To facilitate research in this new instruct-ReID task,
we propose a large-scale OmniReID++ benchmark equipped with diverse data and
comprehensive evaluation methods e.g., task specific and task-free evaluation
settings. In the task-specific evaluation setting, gallery sets are categorized
according to specific ReID tasks. We propose a novel baseline model, IRM, with
an adaptive triplet loss to handle various retrieval tasks within a unified
framework. For task-free evaluation setting, where target person images are
retrieved from task-agnostic gallery sets, we further propose a new method
called IRM++ with novel memory bank-assisted learning. Extensive evaluations of
IRM and IRM++ on OmniReID++ benchmark demonstrate the superiority of our
proposed methods, achieving state-of-the-art performance on 10 test sets. The
datasets, the model, and the code will be available at
https://github.com/hwz-zju/Instruct-ReID
