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Recurrent connections facilitate occluded object recognition by explaining-away.

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Recurrent neural networks can explain away occlusions in visual processing by using information about the occluder. This computational mechanism aids in recognizing objects even when parts are hidden, improving visual perception.

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Cognitive Science

Background:

  • Recurrent connections in the brain are crucial for visual processing but less understood than feedforward connections.
  • Object recognition, especially with occlusions, is a key cognitive function potentially relying on recurrent processing.

Purpose of the Study:

  • To investigate the role and mechanism of recurrent processing in recognizing occluded objects.
  • To demonstrate how recurrent networks can leverage occluder information to improve object recognition.

Main Methods:

  • Utilized convolutional models of the visual system, including those approximating the primate visual system.
  • Conducted a systematic architecture sweep of convolutional models.
  • Performed a human psychophysics experiment to find evidence of the proposed mechanism.

Main Results:

  • Recurrent networks exhibit a unique 'explaining-away' computation, unlike feedforward networks.
  • This computation uses occluder information to account for missing features, aiding occluded object recognition.
  • Evidence consistent with 'explaining-away' was found in both computational models and human experiments.

Conclusions:

  • Recurrent connections possess a distinct computational capability to 'explain-away' occlusions, enhancing visual recognition.
  • This mechanism offers a potential explanation for how the brain handles partially obscured objects.
  • The 'explaining-away' computation may generalize to other perceptual tasks involving context-dependent representation changes.