AIMC Topic: Pattern Recognition, Visual
Clear Filters Showing 31 to 40 of 145 articles
A separable neural code in monkey IT enables perfect CAPTCHA decoding.
Journal of neurophysiology
Feb 23, 2022
Reading distorted letters is easy for us but so challenging for the machine vision that it is used on websites as CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart). How does our brain solve this problem? One solutio...
Employing automatic content recognition for teaching methodology analysis in classroom videos.
PloS one
Feb 17, 2022
A teacher plays a pivotal role in grooming a society and paves way for its social and economic developments. Teaching is a dynamic role and demands continuous adaptation. A teacher adopts teaching techniques suitable for a certain discipline and a si...
A self-supervised domain-general learning framework for human ventral stream representation.
Nature communications
Jan 25, 2022
Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general ...
Visual prototypes in the ventral stream are attuned to complexity and gaze behavior.
Nature communications
Nov 18, 2021
Early theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual ...
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.
Nature communications
Nov 9, 2021
In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex wi...
Joint representation of color and form in convolutional neural networks: A stimulus-rich network perspective.
PloS one
Jun 30, 2021
To interact with real-world objects, any effective visual system must jointly code the unique features defining each object. Despite decades of neuroscience research, we still lack a firm grasp on how the primate brain binds visual features. Here we ...
Representational Content of Oscillatory Brain Activity during Object Recognition: Contrasting Cortical and Deep Neural Network Hierarchies.
eNeuro
May 25, 2021
Numerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theori...
Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Mar 31, 2021
A visual object is characterized by multiple visual features, including its identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pat...
Qualitative similarities and differences in visual object representations between brains and deep networks.
Nature communications
Mar 25, 2021
Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human percept...
Within-category representational stability through the lens of manipulable objects.
Cortex; a journal devoted to the study of the nervous system and behavior
Feb 5, 2021
Our ability to recognize an object amongst many exemplars is one of our most important features, and one that putatively distinguishes humans from non-human animals and potentially from (current) computational and artificial intelligence models. We c...
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