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Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning

  • Sara ElkerdawyEmail author
  • Nilanjan Ray
  • Hong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Vehicle fine-grained classification is a challenging research problem with little attention in the field. In this paper, we propose a deep network architecture for vehicles fine-grained classification without the need of parts or 3D bounding boxes annotation. Co-occurrence layer (COOC) layer is exploited for unsupervised parts discovery. In addition, a two-step procedure with transfer learning and fine-tuning is utilized. This enables us to better fine-tune models with pre-trained weights on ImageNet in some layers while having random initialization in some others. Our model achieves 86.5% accuracy outperforming the state of the art methods in BoxCars116K by 4%. In addition, we achieve 95.5% and 93.19% on CompCars on both train-test splits, 70-30 and 50-50, outperforming the other methods by 4.5% and 8% respectively.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computing Science DepartmentAlberta UniversityEdmontonCanada

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