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On Hyperbolic Embeddings in Object Detection

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Pattern Recognition (DAGM GCPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13485))

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Abstract

Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype. In this work, we study whether a hyperbolic geometry better matches the underlying structure of the object classification space. We incorporate a hyperbolic classifier in two-stage, keypoint-based, and transformer-based object detection architectures and evaluate them on large-scale, long-tailed, and zero-shot object detection benchmarks. In our extensive experimental evaluations, we observe categorical class hierarchies emerging in the structure of the classification space, resulting in lower classification errors and boosting the overall object detection performance.

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Author information

Authors and Affiliations

  1. Robot Learning Lab, University of Freiburg, Freiburg, Germany

    Christopher Lang, Alexander Braun, Lars Schillingmann & Abhinav Valada

Authors
  1. Christopher Lang
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  2. Alexander Braun
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  3. Lars Schillingmann
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  4. Abhinav Valada
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Corresponding author

Correspondence to Christopher Lang .

Editor information

Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Björn Andres

  2. University of Bonn, Bonn, Germany

    Florian Bernard

  3. Technical University of Munich, Munich, Germany

    Daniel Cremers

  4. University of Hamburg, Hamburg, Germany

    Simone Frintrop

  5. University of Konstanz, Konstanz, Germany

    Bastian Goldlücke

  6. University of Siegen, Siegen, Germany

    Ivo Ihrke

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Lang, C., Braun, A., Schillingmann, L., Valada, A. (2022). On Hyperbolic Embeddings in Object Detection. In: Andres, B., Bernard, F., Cremers, D., Frintrop, S., Goldlücke, B., Ihrke, I. (eds) Pattern Recognition. DAGM GCPR 2022. Lecture Notes in Computer Science, vol 13485. Springer, Cham. https://doi.org/10.1007/978-3-031-16788-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-16788-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16787-4

  • Online ISBN: 978-3-031-16788-1

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