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Utilizing Patch-Level Category Activation Patterns for Multiple Class Novelty Detection

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12355))

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Abstract

For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness. This problem of detecting novel class samples during inference is commonly referred to as Multiple Class Novelty Detection. In this paper, we propose a novel method that makes deep convolutional neural networks robust to novel classes. Specifically, during training one branch performs traditional classification (referred to as global inference), and the other branch provides patch-level information to keep track of the class-specific activation patterns (referred to as local inference). Both global and local branch information are combined to train a novelty detection network, which is used during inference to identify novel classes. We evaluate the proposed method on four datasets (Caltech256, CUB-200, Stanford Dogs and FounderType-200) and show that the proposed method is able to identify novel class samples better compared to the other deep convolutional neural network-based methods.

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Notes

  1. 1.

    github.com/tensorflow/tpu/tree/master/models/official/efficientnet.

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Acknowledgement

This work was supported by the NSF grant 1910141.

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Authors and Affiliations

  1. Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA

    Poojan Oza & Vishal M. Patel

Authors
  1. Poojan Oza
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  2. Vishal M. Patel
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Correspondence to Poojan Oza .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Oza, P., Patel, V.M. (2020). Utilizing Patch-Level Category Activation Patterns for Multiple Class Novelty Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12355. Springer, Cham. https://doi.org/10.1007/978-3-030-58607-2_25

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  • DOI: https://doi.org/10.1007/978-3-030-58607-2_25

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