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Part-Based R-CNNs for Fine-Grained Category Detection

  • Ning Zhang
  • Jeff Donahue
  • Ross Girshick
  • Trevor Darrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-normalized representations have been proposed, but generally presume bounding box annotations at test time due to the difficulty of object detection. We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. Our method learns whole-object and part detectors, enforces learned geometric constraints between them, and predicts a fine-grained category from a pose-normalized representation. Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test time.

Keywords

Fine-grained recognition object detection convolutional models 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ning Zhang
    • 1
  • Jeff Donahue
    • 1
  • Ross Girshick
    • 1
  • Trevor Darrell
    • 1
  1. 1.University of CaliforniaBerkeleyUSA

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