AIMC Topic: Pattern Recognition, Visual

Clear Filters Showing 41 to 50 of 152 articles

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.

Nature communications
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
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
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
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
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
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...

Capturing human categorization of natural images by combining deep networks and cognitive models.

Nature communications
Human categorization is one of the most important and successful targets of cognitive modeling, with decades of model development and assessment using simple, low-dimensional artificial stimuli. However, it remains unclear how these findings relate t...

On the robustness of skeleton detection against adversarial attacks.

Neural networks : the official journal of the International Neural Network Society
Human perception of an object's skeletal structure is particularly robust to diverse perturbations of shape. This skeleton representation possesses substantial advantages for parts-based and invariant shape encoding, which is essential for object rec...

Probing the neural dynamics of mnemonic representations after the initial consolidation.

NeuroImage
Memories are not stored as static engrams, but as dynamic representations affected by processes occurring after initial encoding. Previous studies revealed changes in activity and mnemonic representations in visual processing areas, parietal lobe, an...

Improved object recognition using neural networks trained to mimic the brain's statistical properties.

Neural networks : the official journal of the International Neural Network Society
The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. As they are trained for...