Article

Contrast invariant tuning in human perception of image content

2019-07-23

Abstract excerpt

Higher levels of visual processing are progressively more invariant to low-level visual factors such as contrast. Although this invariance trend has been well documented for simple stimuli like gratings and lines, it is difficult to characterize such invariances in images with naturalistic complexity. Here, we use a generative image model based on a hierarchy of learned visual features—a Generative Adversarial Net...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5ccaa2c7-7946-5c0f-8ec3-89e0ad63f227
DOI: 10.1101/711804

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Contrast invariant tuning in human perception of image contentDOI 10.1101/711804Read

Human Sensitivity to Perturbations Constrained by a Model of the Natural Image ManifoldSemantic proximity Read

Constrained sampling from deep generative image models reveals mechanisms of human target detectionSemantic proximity Read

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus spaceSemantic proximity Read

Decoupling lower-level and higher-level visual features in naturalistic scenesSemantic proximity Read

Probing Human Categorization of Naturalistic Images by Combining Deep Neural Networks with Cognitive ModelsSemantic proximity Read

An image-computable model of human visual shape similaritySemantic proximity Read

A parametric texture model based on deep convolutional features closely matches texture appearance for humansSemantic proximity Read

TYCOV: A Year-Long Open Behavioral Study on Human Ability to Detect AI-Generated ImagesSemantic proximity Read

Robust deep learning object recognition models rely on low frequency information in natural imagesSemantic proximity Read

The SPOT Grid: Naturalistic Images with Predefined Settings, Patterns, Objects, and TexturesSemantic proximity Read

The SPOT Grid: Naturalistic Images with Predefined Settings, Patterns, Objects, and TexturesSemantic proximity Read

Deep networks may capture biological behaviour for shallow, but not deep, empirical characterizationsSemantic proximity Read