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Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Scale and translation-invariance for novel objects in human vision.

Yena Han¹, Gemma Roig^2,3, Gad Geiger²

¹Center for Brains, Minds and Machines, MIT, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America. yenahan@mit.edu.

Scientific Reports

|January 31, 2020

Summary

Humans demonstrate remarkable scale-invariance in object recognition after just one exposure. However, translation-invariance is limited, suggesting the visual system

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Area of Science:

Cognitive Neuroscience
Computer Vision
Human Visual Perception

Background:

Object recognition is fundamental to human vision.
Understanding invariance (tolerance to changes in scale, position, etc.) is crucial but challenging.
Current computational models, like deep learning, often require extensive data for robust recognition.

Purpose of the Study:

To investigate the extent of scale- and translation-invariance in one-shot learning of novel objects.
To compare human visual system strategies with computational models, particularly deep learning architectures.
To elucidate the neural computations underlying invariant object recognition.

Main Methods:

Psychophysical experiments using Korean letters presented to naive subjects.
Measuring recognition accuracy under varying scales and positions.
Comparing experimental data with computational modeling of neural networks.

Main Results:

Humans exhibit significant scale-invariance after a single exposure to a novel object.
Translation-invariance is limited and depends on object size and position.
Neural network models require explicit scale-invariance mechanisms (scale channels, eccentricity-dependent representations) to match human performance.

Conclusions:

The human visual system achieves data-efficient, invariant object recognition through strategies distinct from current deep learning models.
Incorporating scale-invariance and eccentricity-dependent representations is key for computational models.
Eye movements play a critical role in the human visual system's invariant recognition capabilities.