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Related Concept Videos

Observational Learning01:12

Observational Learning

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 because...
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Cognitive Learning01:21

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Related Experiment Video

Updated: Jun 12, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

NOTO: Noise-Tolerate Evidential Learning for Open-Set Cross-Modal Retrieval.

Ruitao Pu, Chao Su, Peng Hu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework for cross-modal retrieval (CMR) that effectively handles noisy labels, including open-set noisy labels (OSNL). The proposed method, NOTO, enhances representation learning for robust CMR performance even with imperfect data.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
    05:48

    Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

    Published on: August 9, 2024

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Cross-modal retrieval (CMR) methods often rely on clean annotations and closed-set assumptions, which are unrealistic.
    • Real-world data frequently contains label noise and emergent open-set categories, termed open-set noisy labels (OSNL).
    • OSNL poses significant challenges for CMR by forcing models to learn incorrect semantic associations, degrading performance.

    Purpose of the Study:

    • To develop a robust framework for cross-modal retrieval (CMR) that can effectively handle both closed-set and open-set noisy labels (OSNL).
    • To improve the learning of shared cross-modal representations in the presence of imperfect annotations.
    • To enhance the performance and robustness of CMR systems in practical, real-world scenarios.

    Main Methods:

    • Proposes NOise-TOlerate evidential learning (NOTO), a novel framework for robust cross-modal representation learning.
    • Introduces a Robust Evidential Learning (REL) module to detect and classify instances as clean, closed-set noisy, or open-set noisy using Dirichlet evidence and belief masses.
    • Develops an Adaptive Noise-aware Contrast (ANC) module to select reliable positive pairs and maximize mutual information for improved cross-modal alignment.

    Main Results:

    • The NOTO framework demonstrates superior retrieval performance compared to ten state-of-the-art CMR methods.
    • NOTO exhibits significant robustness against open-set noisy labels (OSNL).
    • Experiments conducted on four benchmarks validate the effectiveness of the proposed approach.

    Conclusions:

    • The NOTO framework offers a robust solution for cross-modal retrieval (CMR) under challenging noisy label conditions, including open-set noisy labels (OSNL).
    • The proposed REL and ANC modules effectively address the limitations of existing CMR methods in realistic data scenarios.
    • NOTO significantly advances the state-of-the-art in robust cross-modal representation learning and retrieval.