244 research outputs found
Discovering Discriminative Geometric Features with Self-Supervised Attention for Vehicle Re-Identification and Beyond
In the literature of vehicle re-identification (ReID), intensive manual
labels such as landmarks, critical parts or semantic segmentation masks are
often required to improve the performance. Such extra information helps to
detect locally geometric features as a part of representation learning for
vehicles. In contrast, in this paper, we aim to address the challenge of {\em
automatically} learning to detect geometric features as landmarks {\em with no
extra labels}. To the best of our knowledge, we are the {\em first} to
successfully learn discriminative geometric features for vehicle ReID based on
self-supervised attention. Specifically, we implement an end-to-end trainable
deep network architecture consisting of three branches: (1) a global branch as
backbone for image feature extraction, (2) an attentional branch for producing
attention masks, and (3) a self-supervised branch for regularizing the
attention learning with rotated images to locate geometric features. %Our
network design naturally leads to an end-to-end multi-task joint optimization.
We conduct comprehensive experiments on three benchmark datasets for vehicle
ReID, \ie VeRi-776, CityFlow-ReID, and VehicleID, and demonstrate our
state-of-the-art performance. %of our approach with the capability of capturing
informative vehicle parts with no corresponding manual labels. We also show the
good generalization of our approach in other ReID tasks such as person ReID and
multi-target multi-camera (MTMC) vehicle tracking. {\em Our demo code is
attached in the supplementary file.
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