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PixOOD: Pixel-Level Out-of-Distribution Detection

  • Conference paper
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Computer Vision – ECCV 2024 (ECCV 2024)

Abstract

We propose a pixel-level out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD.

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Acknowledgement

This work was supported by Toyota Motor Europe and Czech Technical University in Prague institutional support Future Fund.

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Correspondence to Tomáš Vojíř .

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Vojíř, T., Šochman, J., Matas, J. (2025). PixOOD: Pixel-Level Out-of-Distribution Detection. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15118. Springer, Cham. https://doi.org/10.1007/978-3-031-73027-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-73027-6_6

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