A Versatile Framework for Multi-scene Person Re-identification
Wei-Shi Zheng Junkai Yan Yi-Xing Peng
Person Re-identification (ReID) has been extensively developed for a decade
in order to learn the association of images of the same person across
non-overlapping camera views. To overcome significant variations between images
across camera views, mountains of variants of ReID models were developed for
solving a number of challenges, such as resolution change, clothing change,
occlusion, modality change, and so on. Despite the impressive performance of
many ReID variants, these variants typically function distinctly and cannot be
applied to other challenges. To our best knowledge, there is no versatile ReID
model that can handle various ReID challenges at the same time. This work
contributes to the first attempt at learning a versatile ReID model to solve
such a problem. Our main idea is to form a two-stage prompt-based twin modeling
framework called VersReID. Our VersReID firstly leverages the scene label to
train a ReID Bank that contains abundant knowledge for handling various scenes,
where several groups of scene-specific prompts are used to encode different
scene-specific knowledge. In the second stage, we distill a V-Branch model with
versatile prompts from the ReID Bank for adaptively solving the ReID of
different scenes, eliminating the demand for scene labels during the inference
stage. To facilitate training VersReID, we further introduce the multi-scene
properties into self-supervised learning of ReID via a multi-scene prioris data
augmentation (MPDA) strategy. Through extensive experiments, we demonstrate the
success of learning an effective and versatile ReID model for handling ReID
tasks under multi-scene conditions without manual assignment of scene labels in
the inference stage, including general, low-resolution, clothing change,
occlusion, and cross-modality scenes. Codes and models are available at
https://github.com/iSEE-Laboratory/VersReID.