Ultra-rapid visual search in natural images using active deep learning

👉🏼 Jean-Nicolas Jérémie @JnJerem
📍Poster session at #FENS2022
🎯 Ultra-rapid visual search in natural images using active deep learning.#FENSAmbassador @SocNeuro_Tweets @laurentperrinet 1/2 pic.twitter.com/ldGJMCQb4c

— Mélina Cordeau (@CordeauMelina) July 11, 2022

• This work extends to natural scenes a previous work on visual search on a simplified task formulated in
  Emmanuel Daucé, Pierre Albigès, Laurent U Perrinet (2020). A dual foveal-peripheral visual processing model implements efficient saccade selection. Journal of Vision.

  Cite DOI Pdf Code URL bioRxiv

• follows
  Jean-Nicolas Jérémie, Emmanuel Daucé, Laurent U Perrinet (2022). Ultra-rapid visual search in natural images using active deep learning. Proceedings of AREADNE.

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• It is based on a first work on transfer learning and its application to a natural task :
  Jean-Nicolas Jérémie, Laurent U Perrinet (2023). Ultra-Fast Image Categorization in biology and in neural models. Vision.

  Cite DOI arXiv

• in particular, we found retinotopic mapping to be adapted to that extension :
  Jean-Nicolas Jérémie, Emmanuel Daucé, Laurent U Perrinet (2022). Retinotopic mapping improves the reliability of image classification. NeuroVision Workshop in conjunction with CVPR 2022.

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