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

Associative Learning01:27

Associative Learning

300
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...
300
Observational Learning01:12

Observational Learning

141
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
141
Introduction to Learning01:18

Introduction to Learning

337
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
337
Purposive Learning01:22

Purposive Learning

101
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
101
Language Development01:22

Language Development

318
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
318
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.2K

You might also read

Related Articles

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

Sort by
Same author

[Effects of electroacupuncture at "Neiguan" (PC 6) on p38 MAPK signaling pathway in rats with cardiac hypertrophy].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2012
Same author

Accurate measurement of oxygen consumption in children undergoing cardiac catheterization.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2012
Same author

Glutathione S-transferase polymorphisms and bone tumor risk in China.

Asian Pacific journal of cancer prevention : APJCP·2012
Same author

Systemic oxygen transport derived by using continuous measured oxygen consumption after the Norwood procedure-an interim review.

Interactive cardiovascular and thoracic surgery·2012
Same author

Discovery and optimization of 2,4-diaminoquinazoline derivatives as a new class of potent dengue virus inhibitors.

Journal of medicinal chemistry·2012
Same author

β(3)-Adrenoceptor Antagonist SR59230A Attenuates the Imbalance of Systemic and Myocardial Oxygen Transport Induced by Dopamine in Newborn Lambs.

Clinical Medicine Insights. Cardiology·2012
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2025

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

505

Language-Inspired Relation Transfer for Few-Shot Class-Incremental Learning.

Yifan Zhao, Jia Li, Zeyin Song

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 6, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new Language-inspired Relation Transfer (LRT) method for Few-Shot Class-Incremental Learning (FSCIL). LRT enhances object recognition by combining visual and text data, outperforming existing models.

    More Related Videos

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

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

    Published on: February 8, 2019

    6.5K
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.5K

    Related Experiment Videos

    Last Updated: Jun 8, 2025

    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

    505
    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

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

    Published on: February 8, 2019

    6.5K
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.5K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Few-Shot Class-Incremental Learning (FSCIL) aims to enable systems to learn new classes from limited examples while retaining existing knowledge.
    • Current FSCIL methods often struggle with a trade-off between base and incremental knowledge due to reliance on visual encoder tuning.
    • Human learning effectively incorporates language descriptions for recognizing novel concepts, a capability lacking in many AI systems.

    Purpose of the Study:

    • To propose a novel Language-inspired Relation Transfer (LRT) paradigm for Few-Shot Class-Incremental Learning (FSCIL).
    • To leverage both visual cues and textual descriptions for improved object understanding and classification in open-world settings.
    • To overcome the limitations of existing methods by addressing the knowledge trade-off and domain gap in incremental learning.

    Main Methods:

    • Developed a two-step LRT paradigm integrating visual and language information.
    • Introduced a graph relation transformation module to transfer pre-trained text knowledge to visual domains.
    • Implemented a text-vision prototypical fusion module for combining visual and language embeddings.
    • Utilized context prompt learning for rapid domain alignment and imagined contrastive learning to address limited text data during alignment.

    Main Results:

    • The proposed LRT paradigm demonstrated superior performance compared to state-of-the-art models.
    • Achieved over 13% improvement on the miniImageNet FSCIL benchmark.
    • Achieved over 7% improvement on the CIFAR-100 FSCIL benchmark.

    Conclusions:

    • The LRT paradigm effectively enhances Few-Shot Class-Incremental Learning by synergizing visual and language modalities.
    • The proposed methods for domain alignment and text-image transfer successfully mitigate challenges in incremental learning.
    • LRT offers a promising direction for developing more robust and adaptable AI systems capable of lifelong learning.