Recent Advances at the Brain-Driven Computer Vision Workshop 2018

  • Simone PalazzoEmail author
  • Isaak Kavasidis
  • Dimitris Kastaniotis
  • Stavros Dimitriadis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)


The 1\(^\text {st}\) edition of the Brain-Driven Computer Vision Workshop, held in Munich in conjunction with the European Conference on Computer Vision 2018, aimed at attracting, promoting and inspiring research on paradigms, methods and tools for computer vision driven or inspired by the human brain. While successful, in terms of the quality of received submissions and audience present at the event, the workshop emphasized some of the factors that currently limit research in this field. In this report, we discuss the success points of the workshop, the characteristics of the presented works, and our considerations on the state of current research and future directions of research in this topic.


Brain-Driven Computer Vision Biologically-inspired machine learning 

List of Workshop Papers

  1. 1.
    Dwivedi, K., Roig, G.: Navigational affordance cortical responses explained by scene-parsing modelGoogle Scholar
  2. 2.
    Vascon, S., Parin, Y., Annavini, E., D’Andola, M., Zoccolan, D., Pelillo, M.: Characterization of visual object representations in rat primary visual cortexGoogle Scholar
  3. 3.
    Papadimitriou, A., Passalis, N., Tefas, A.: Decoding generic visual representations from human brain activity using machine learningGoogle Scholar
  4. 4.
    Jaiswal, A., AbdAlmageed, W., Wu, Y., Natarajan, P.: Capsulegan: Generative adversarial capsule networkGoogle Scholar
  5. 5.
    Ramasinghe, S., Athuraliya, C., Khan, S.: A context-aware capsule network for multi-label classificationGoogle Scholar
  6. 6.
    Strisciuglio, N., Azzopardi, G., Petkov, N.: Brain-inspired robust delineation operatorGoogle Scholar
  7. 7.
    Natsume, R., Inoue, K., Fukuhara, Y., Yamamoto, S., Morishima, S., Kataoka, H.: Understanding fake facesGoogle Scholar
  8. 8.
    Wever, R., Runia, T.F.: Subitizing with variational autoencodersGoogle Scholar
  9. 9.
    Dias, C., Dimiccoli, M.: Learning event representations by encoding the temporal contextGoogle Scholar
  10. 10.
    Nardo, E.D., Petrosino, A., Ullah, I.: Emop3d: A brain like pyramidal deep neural network for emotion recognitionGoogle Scholar


  1. 11.
    Horikawa, T., Kamitani, Y.: Generic decoding of seen and imagined objects using hierarchical visual features. Nat. Commun. 8, 15037 (2017)CrossRefGoogle Scholar
  2. 12.
    Cichy, R.M., Khosla, A., Pantazis, D., Torralba, A., Oliva, A.: Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6, 27755 (2016)CrossRefGoogle Scholar
  3. 13.
    Bullier, J.: Integrated model of visual processing. Brain Res. Brain Res. Rev. 36(2–3), 96–107 (2001)CrossRefGoogle Scholar
  4. 14.
    Kourtzi, Z., Connor, C.E.: Neural representations for object perception: structure, category, and adaptive coding. Annu. Rev. Neurosci. 34, 45–67 (2011)CrossRefGoogle Scholar
  5. 15.
    Kravitz, D.J., Saleem, K.S., Baker, C.I., Mishkin, M.: A new neural framework for visuospatial processing. Nat. Rev. Neurosci. 12(4), 217–230 (2011)CrossRefGoogle Scholar
  6. 16.
    DiCarlo, J.J., Zoccolan, D., Rust, N.C.: How does the brain solve visual object recognition? Neuron 73(3), 415–434 (2012)CrossRefGoogle Scholar
  7. 17.
    Wen, H., Han, K., Shi, J., Zhang, Y., Culurciello, E., Liu, Z.: Deep predictive coding network for object recognition. In: Dy, J., Krause, A., (eds.) Proceedings of the 35th International Conference on Machine Learning. Volume 80 of Proceedings of Machine Learning Research, Stockholmsmässan, Stockholm Sweden, PMLR, 10–15 July 2018, pp. 5266–5275 (2018)Google Scholar
  8. 18.
    Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36(3), 181–204 (2013)CrossRefGoogle Scholar
  9. 19.
    Bastos, A.M., Usrey, W.M., Adams, R.A., Mangun, G.R., Fries, P., Friston, K.J.: Canonical microcircuits for predictive coding. Neuron 76(4), 695–711 (2012)CrossRefGoogle Scholar
  10. 20.
    Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Souly, N., Shah, M.: Deep learning human mind for automated visual classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, pp. 4503–4511 (2017)Google Scholar
  11. 21.
    Palazzo, S., Spampinato, C., Kavasidis, I., Giordano, D., Shah, M.: Generative adversarial networks conditioned by brain signals. In: IEEE International Conference on Computer Vision (ICCV), October 2017, pp. 3430–3438 (2017)Google Scholar
  12. 22.
    Nishimoto, S., Vu, A.T., Naselaris, T., Benjamini, Y., Yu, B., Gallant, J.L.: Reconstructing visual experiences from brain activity evoked by natural movies. Curr. Biol. 21(19), 1641–1646 (2011)CrossRefGoogle Scholar
  13. 23.
    Stansbury, D.E., Naselaris, T., Gallant, J.L.: Natural scene statistics account for the representation of scene categories in human visual cortex. Neuron 79(5), 1025–1034 (2013)CrossRefGoogle Scholar
  14. 24.
    Kavasidis, I., Palazzo, S., Spampinato, C., Giordano, D., Shah, M.: Brain2Image: converting brain signals into images. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 1809–1817. ACM (2017)Google Scholar
  15. 25.
    Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607 (1997)CrossRefGoogle Scholar
  16. 26.
    Jhuang, H., Serre, T., Wolf, L., Poggio, T.: A biologically inspired system for action recognition. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  17. 27.
    Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 3856–3866. Curran Associates, Inc. (2017)Google Scholar
  18. 28.
    Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  19. 29.
    Schapiro, A.C., Rogers, T.T., Cordova, N.I., Turk-Browne, N.B., Botvinick, M.M.: Neural representations of events arise from temporal community structure. Nat. Neurosci. 16(4), 486 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Simone Palazzo
    • 1
    Email author
  • Isaak Kavasidis
    • 1
  • Dimitris Kastaniotis
    • 2
  • Stavros Dimitriadis
    • 3
  1. 1.University of CataniaCataniaItaly
  2. 2.University of PatrasPatrasGreece
  3. 3.Cardiff UniversityCardiffUK

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