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Companion Paper: Deep Saliency Map Generators for Multispectral Video Classification

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Reproducible Research in Pattern Recognition (RRPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14068))

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Abstract

This is the companion paper for the ICPR 2022 Paper “Deep Saliency Map Generators for Multispectral Video Classification”, that investigates the applicability of three saliency map generators on multispectral video input data. In addition to implementation details of modifications for the investigated methods and the used neural network implementations, the influence of the parameters and a more detailed insight in the training and evaluation process is given.

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Notes

  1. 1.

    https://github.com/JensBayer/ICPR2022.

  2. 2.

    https://github.com/eclique/RISE.

  3. 3.

    https://github.com/satyamahesh84/SIDU_XAI_CODE.

  4. 4.

    https://github.com/zhang-can/PAN-PyTorch.

References

  1. Bayer, J., Munch, D., Arens, M.: Deep Saliency Map Generators for Multispectral Video Classification. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 3757–3764. IEEE (8 2022). https://doi.org/10.1109/ICPR56361.2022.9955639. https://ieeexplore.ieee.org/document/9955639/

  2. Muddamsetty, S.M., Mohammad, N.S.J., Moeslund, T.B.: SIDU: Similarity Difference And Uniqueness Method for Explainable AI. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 3269–3273. IEEE (10 2020). https://ieeexplore.ieee.org/document/9190952/

  3. Petsiuk, V., Das, A., Saenko, K.: RISE: Randomized input sampling for explanation of black-box models. In: British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  4. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128(2), 336–359 (2019). https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

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Acknowledgements

This work was developed in Fraunhofer Cluster of Excellence “Cognitive Internet Technologies”.

Author information

Authors and Affiliations

  1. Fraunhofer Center for Machine Learning, Sankt Augustin, Germany

    Jens Bayer, David Münch & Michael Arens

  2. Fraunhofer IOSB, Gutleuthausstr. 1, 76275, Ettlingen, Germany

    Jens Bayer, David Münch & Michael Arens

Authors
  1. Jens Bayer
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  2. David Münch
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  3. Michael Arens
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Corresponding author

Correspondence to Jens Bayer .

Editor information

Editors and Affiliations

  1. Lumière University Lyon 2, Bron, France

    Bertrand Kerautret

  2. Ecole Normale Superieure Paris, Gif-sur-Yvette, France

    Miguel Colom

  3. University of Stasbourg, Illkirch Cedex, France

    Adrien Krähenbühl

  4. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  5. Ecole des Ponts Paris Tech, Marne- la- Vallée Cedex 2, France

    Pascal Monasse

  6. ESIEE Paris, Université Gustave Eiffel, Noisy le Grand Cedex, France

    Benjamin Perret

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Bayer, J., Münch, D., Arens, M. (2023). Companion Paper: Deep Saliency Map Generators for Multispectral Video Classification. In: Kerautret, B., Colom, M., Krähenbühl, A., Lopresti, D., Monasse, P., Perret, B. (eds) Reproducible Research in Pattern Recognition. RRPR 2022. Lecture Notes in Computer Science, vol 14068. Springer, Cham. https://doi.org/10.1007/978-3-031-40773-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-40773-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40772-7

  • Online ISBN: 978-3-031-40773-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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