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

52.8K
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.
52.8K
Associative Learning01:27

Associative Learning

270
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
270
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

499
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
499
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

5.5K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
5.5K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

371
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
371
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K

You might also read

Related Articles

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

Sort by
Same author

An interpretable memory forensics framework for unknown attack identification in power grid edge devices.

Scientific reports·2026
Same author

Underload in sky: reducing the passive fatigue of pilots during cruise through an attention guided - active interactive seating system.

Ergonomics·2026
Same author

Fermented feed-rumen microbiota axis mediates goat kid growth via microbial amino acid synthesis and ruminal epithelial energy metabolism.

NPJ biofilms and microbiomes·2026
Same author

Functional Polyesters: Tailoring Structure and Biomedical Functions.

Polymer science & technology (Washington, D.C.)·2026
Same author

A nucleic acid regulation strategy under mechanical stress for intervertebral disc degeneration treatment.

Bioactive materials·2026
Same author

Natural variation in <i>PtoCPK3</i> governs drought tolerance by orchestrating xylem remodeling and lignin metabolism in <i>Populus</i>.

Science advances·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: May 20, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

251

Class-Specific Prompt Learning for Vision-Language Models.

Runhao Li, Yongming Chen, Zhenyu Weng

    IEEE Transactions on Neural Networks and Learning Systems
    |May 16, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Class-specific prompt learning (CPL) adapts vision-language models (VLMs) using tailored prompts, improving few-shot learning. This method enhances performance by capturing class-specific details, outperforming universal prompts.

    More Related Videos

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    474
    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

    1.3K

    Related Experiment Videos

    Last Updated: May 20, 2025

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    251
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    474
    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

    1.3K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Vision-language models (VLMs) are adapted for downstream tasks using prompt learning, offering a cost-effective alternative to fine-tuning.
    • Existing methods often use universal prompts, which may not capture crucial class-specific information for optimal performance.

    Purpose of the Study:

    • To introduce Class-Specific Prompt Learning (CPL) to enhance VLM adaptation for downstream tasks.
    • To improve the discriminative power of prompts by incorporating class-specific information.

    Main Methods:

    • CPL utilizes a dual-component prompt: a shared base vector for generalization and a class-specific vector for adaptability.
    • Contrastive CPL and self-consistency loss are employed to refine prompt discriminative features and base context generalization.

    Main Results:

    • CPL effectively learns tailored prompts for individual classes, enhancing feature discrimination.
    • Experiments show CPL surpasses existing methods in both base-class classification and new class generalization.

    Conclusions:

    • Class-specific prompt learning offers a significant advancement in adapting VLMs.
    • CPL provides a more effective approach for few-shot learning by leveraging class-specific contextual information.