Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Vision01:24

Vision

61.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
61.3K
Visual System01:26

Visual System

2.2K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
2.2K
Color Vision01:24

Color Vision

1.9K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.9K
Retrieval01:12

Retrieval

512
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
512
Visual Agnosia01:12

Visual Agnosia

1.6K
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...
1.6K
Prosopagnosia01:24

Prosopagnosia

1.1K
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

DrawMotion: Generating 3D Human Motions by Freehand Drawing.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

MC#: Mixture Compressor for Mixture-of-Experts Large Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

UniVST: A Unified Framework for Training-Free Localized Video Style Transfer.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Toward Open-World Domain Adaptation via Iteratively Contrastive Learning and Clustering.

IEEE transactions on neural networks and learning systems·2025
Same author

Gamba: Marry Gaussian Splatting With Mamba for Single-View 3D Reconstruction.

IEEE transactions on pattern analysis and machine intelligence·2025

Related Experiment Video

Updated: Mar 23, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Cross-Modal Retrieval With CNN Visual Features: A New Baseline.

Yunchao Wei, Yao Zhao, Canyi Lu

    IEEE Transactions on Cybernetics
    |April 6, 2016
    PubMed
    Summary

    Convolutional Neural Network (CNN) visual features offer a powerful, universal representation for recognition tasks. This study demonstrates their effectiveness in cross-modal retrieval, outperforming traditional methods.

    More Related Videos

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.5K

    Related Experiment Videos

    Last Updated: Mar 23, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.2K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.5K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Convolutional Neural Networks (CNNs) provide robust visual features for various recognition tasks.
    • Cross-modal retrieval aims to retrieve relevant information across different data modalities (e.g., image and text).

    Purpose of the Study:

    • To evaluate the efficacy of CNN visual features for cross-modal retrieval.
    • To enhance CNN feature representation through fine-tuning and propose a novel deep semantic matching method.

    Main Methods:

    • Extracted off-the-shelf CNN visual features from models pre-trained on ImageNet.
    • Fine-tuned CNN models using the Caffe library for specific datasets.
    • Developed a deep semantic matching approach for label-annotated data.

    Main Results:

    • CNN visual features demonstrated superior performance in cross-modal retrieval tasks.
    • Fine-tuning further improved the representational ability of CNN features.
    • The proposed deep semantic matching method effectively addressed retrieval for multi-label data.

    Conclusions:

    • CNN visual features are highly effective for cross-modal retrieval.
    • Fine-tuning and deep semantic matching enhance retrieval accuracy.
    • This approach offers a significant advancement in cross-modal retrieval systems.