AIMC Topic: Visual Perception

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Representation of locomotive action affordances in human behavior, brains, and deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immediate visual environment. The neural basis of our ability to recognize locomotive affordances is unknown...

Beyond binding: from modular to natural vision.

Trends in cognitive sciences
The classical view of visual cortex organization as a collection of specialized modules processing distinct features like color and motion has profoundly influenced neuroscience for decades. This framework, rooted in historical philosophical distinct...

Evaluation of spatial visual perception of streets based on deep learning and spatial syntax.

Scientific reports
Street visual quality improvement plays an important role in urban development. An important direction for street quality research lies in accurately perceiving the spatial quality of urban streets and exploring the connection with street constituent...

Integrating Bayesian and neural networks models for eye movement prediction in hybrid search.

Scientific reports
Visual search is crucial in daily human interaction with the environment. Hybrid search extends this by requiring observers to find any item from a given set. Recently, a few models were proposed to simulate human eye movements in visual search tasks...

Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.

Human brain mapping
While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning ...

Convolutional neural networks uncover the dynamics of human visual memory representations over time.

Cerebral cortex (New York, N.Y. : 1991)
The ability to accurately retrieve visual details of past events is a fundamental cognitive function relevant for daily life. While a visual stimulus contains an abundance of information, only some of it is later encoded into long-term memory represe...

Human Eyes-Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises.

Neural computation
Humans actively observe the visual surroundings by focusing on salient objects and ignoring trivial details. However, computer vision models based on convolutional neural networks (CNN) often analyze visual input all at once through a single feedforw...

Decoding Visual Perception from EEG Using Explainable Graph Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Brain decoding is an emerging area in the fields of neuroscience and machine learning. The goal of decoding is to utilize measured brain activity to understand the thoughts or sensations of individuals. In the fields of computer vision and machine le...

The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.

Journal of vision
In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distorti...

A Critical Test of Deep Convolutional Neural Networks' Ability to Capture Recurrent Processing in the Brain Using Visual Masking.

Journal of cognitive neuroscience
Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convoluti...