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Convolutional Scale Invariance for Semantic Segmentation

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Book cover Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

We propose an effective technique to address large scale variation in images taken from a moving car by cross-breeding deep learning with stereo reconstruction. Our main contribution is a novel scale selection layer which extracts convolutional features at the scale which matches the corresponding reconstructed depth. The recovered scale-invariant representation disentangles appearance from scale and frees the pixel-level classifier from the need to learn the laws of the perspective. This results in improved segmentation results due to more efficient exploitation of representation capacity and training data. We perform experiments on two challenging stereoscopic datasets (KITTI and Cityscapes) and report competitive class-level IoU performance.

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Notes

  1. 1.

    http://multiclod.zemris.fer.hr/kitti_semseg_unizg.shtml.

  2. 2.

    https://github.com/ivankreso/scale-invariant-cnn.

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Acknowledgement

This work has been fully supported by Croatian Science Foundation under the project I-2433-2014. The Titan X used for this research was donated by the NVIDIA Corporation.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Ivan Krešo, Denis Čaušević, Josip Krapac & Siniša Šegvić

Authors
  1. Ivan Krešo
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  2. Denis Čaušević
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  3. Josip Krapac
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  4. Siniša Šegvić
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Corresponding author

Correspondence to Ivan Krešo .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Krešo, I., Čaušević, D., Krapac, J., Šegvić, S. (2016). Convolutional Scale Invariance for Semantic Segmentation. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_6

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