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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision.

Hristofor Lukanov1, Peter König2,3, Gordon Pipa1

  • 1Department of Neuroinformatics, Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany.

Frontiers in Computational Neuroscience
|December 9, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a novel computational model of foveated vision, mimicking primate retino-cortical mapping. This efficient deep learning approach reduces computational load and memory, achieving strong performance in image and video classification tasks.

Keywords:
active visionbottom-up attentiondeep learning-artificial neural network (DL-ANN)foveal visionperipheral visionspace-variant visiontop-down attention

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Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Artificial Intelligence

Background:

  • Foveated vision, common in biology, is underrepresented in computational models and deep learning.
  • Training deep neural networks faces challenges with hardware limitations and model complexity.

Purpose of the Study:

  • To propose an end-to-end neural model for foveal-peripheral vision inspired by primate retino-cortical mapping.
  • To develop an efficient visual signal compression technique using a combination of high and low resolution.
  • To implement an attention mechanism for simulating "eye-movements" to incrementally gather scene information.

Main Methods:

  • An efficient sampling technique compresses visual input, maintaining a large field of view with a high-resolution foveal region.
  • An attention mechanism guides "eye-movements" for incremental information acquisition.
  • A bottom-up and top-down attention mechanism is integrated, leveraging task-relevant features.

Main Results:

  • The model achieves comparable image classification results and superior video classification performance against full-resolution architectures.
  • Significant reduction in computational effort (tenfold) and memory usage.
  • The attention mechanism is a convenient byproduct of the main architecture.

Conclusions:

  • The proposed model offers substantial computational and memory efficiency for foveal-peripheral vision tasks.
  • It provides a viable approach for exploring active vision in agent training for simulated environments and robotics.
  • This work bridges biological vision principles with efficient deep learning architectures.