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Robust Object Detection Using Knowledge Graph Embeddings

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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 recognition for the most part has been treated as a one-hot problem that assumes classes to be discrete and unrelated. In this work, we challenge the prevalence of the one-hot approach in closed-set object detection. We evaluate the error statistics of learned class embeddings from a one-hot approach with knowledge embeddings that inherit semantic structure from natural language processing or knowledge graphs, as widely applied in open world object detection. Extensive experimental results on multiple knowledge-embeddings as well as distance metrics indicate that knowledge-based class representations result in more semantically grounded misclassifications while performing on par compared to one-hot methods on the challenging COCO and Cityscapes object detection benchmarks. We generalize our findings to multiple object detection architectures by proposing a knowledge-embedded design knowledge graph embedded (KGE) for keypoint-based and transformer-based object detection architectures.

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Authors and Affiliations

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

    Christopher Lang, Alexander Braun & Abhinav Valada

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  1. Christopher Lang
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  2. Alexander Braun
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  3. Abhinav Valada
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Corresponding author

Correspondence to Abhinav Valada .

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., Valada, A. (2022). Robust Object Detection Using Knowledge Graph Embeddings. 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_27

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  • DOI: https://doi.org/10.1007/978-3-031-16788-1_27

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