  Person attribute recognition and attribute-based retrieval are two core
human-centric tasks. In the recognition task, the challenge is specifying
attributes depending on a person's appearance, while the retrieval task
involves searching for matching persons based on attribute queries. There is a
significant relationship between recognition and retrieval tasks. In this
study, we demonstrate that if there is a sufficiently robust network to solve
person attribute recognition, it can be adapted to facilitate better
performance for the retrieval task. Another issue that needs addressing in the
retrieval task is the modality gap between attribute queries and persons'
images. Therefore, in this paper, we present CLEAR, a unified network designed
to address both tasks. We introduce a robust cross-transformers network to
handle person attribute recognition. Additionally, leveraging a pre-trained
language model, we construct pseudo-descriptions for attribute queries and
introduce an effective training strategy to train only a few additional
parameters for adapters, facilitating the handling of the retrieval task.
Finally, the unified CLEAR model is evaluated on five benchmarks: PETA, PA100K,
Market-1501, RAPv2, and UPAR-2024. Without bells and whistles, CLEAR achieves
state-of-the-art performance or competitive results for both tasks,
significantly outperforming other competitors in terms of person retrieval
performance on the widely-used Market-1501 dataset.
