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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...

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Related Experiment Video

Updated: Jun 12, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

SIM-OFE: Structure Information Mining and Object-Aware Feature Enhancement for Fine-Grained Visual Categorization.

Hongbo Sun, Xiangteng He, Jinglin Xu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 18, 2024

Summary

This summary is machine-generated.

This study introduces a new method for fine-grained visual categorization (FGVC) to better distinguish subtle differences between object subcategories. The Structure Information Mining and Object-aware Feature Enhancement (SIM-OFE) method improves recognition by analyzing internal object structures and enhancing features.

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Last Updated: Jun 12, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Fine-grained visual categorization (FGVC) is challenging due to subtle inter-class differences.
Current methods often overlook internal object structure, limiting performance.
Effective FGVC requires capturing both global and local discriminative features.

Purpose of the Study:

To propose a novel method for enhancing fine-grained visual categorization.
To address limitations of existing methods by incorporating object structure and appearance.
To improve the accuracy of distinguishing between visually similar subcategories.

Main Methods:

Developed a hybrid perception attention module for object localization using global and local significance.
Introduced a structure information mining module to model critical region distributions and relationships.
Proposed an object-aware feature enhancement module for attentive coupling of global and local features.

Main Results:

The proposed Structure Information Mining and Object-aware Feature Enhancement (SIM-OFE) method was evaluated.
Experiments were conducted on three benchmark FGVC datasets.
The SIM-OFE method achieved state-of-the-art performance.

Conclusions:

The SIM-OFE method effectively captures internal object structure and appearance traits.
The approach enhances discriminative feature learning for fine-grained recognition.
SIM-OFE demonstrates superior performance in fine-grained visual categorization tasks.