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The Devil Is in the Details: Self-supervised Attention for Vehicle Re-identification

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12359)

Abstract

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.

Keywords

Vehicle re-identification Self-supervised learning Variational auto-encoder Deep representation learning 

Notes

Acknowledgement

This research is supported in part by the Northrop Grumman Mission Systems Research in Applications for Learning Machines (REALM) initiative, and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. D17PC00345. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Automation Research, UMIACS, and the Department of Electrical and Computer EngineeringUniversity of MarylandCollege ParkUSA
  2. 2.Research Center for Information Technology Innovation, Academia SinicaTaipeiTaiwan

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