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An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

Li Niu, Wen Li, Dong Xu

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    |November 12, 2016
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    Summary
    This summary is machine-generated.

    This study introduces an exemplar-based multi-view domain generalization (EMVDG) framework for robust visual recognition. The EMVDG framework enhances classifier generalization across different data distributions using multi-view features.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Visual recognition models often struggle with domain shift, where training and testing data distributions differ significantly.
    • Generalizing classifiers to unseen target domains is a key challenge in machine learning.
    • Leveraging multi-view features can improve recognition performance by exploiting relationships between different data modalities.

    Purpose of the Study:

    • To propose a novel exemplar-based multi-view domain generalization (EMVDG) framework for robust visual recognition.
    • To address the challenges of domain shift and improve generalization to arbitrary target domains.
    • To effectively utilize multi-view features for enhanced recognition performance.

    Main Methods:

    • The EMVDG framework is built upon exemplar SVMs (ESVMs), learning multiple classifiers from individual positive samples and all negative samples.
    • Two approaches are proposed: EMVDG_CO enforces consistent classifier clustering across views using co-regularization, and EMVDG_MK fuses classifiers from different views based on complementary principles.
    • The framework is extended to an exemplar-based multi-view domain adaptation (EMVDA) setting when unlabeled target data is available.

    Main Results:

    • Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the EMVDG framework.
    • The proposed EMVDG_CO and EMVDA methods show significant improvements in visual recognition tasks.
    • The study validates the ability of the framework to learn robust classifiers that generalize well to unseen domains.

    Conclusions:

    • The proposed EMVDG framework offers a robust solution for visual recognition under domain shift.
    • Exploiting multi-view features and exemplar-based learning enhances classifier generalization.
    • The EMVDG and EMVDA frameworks provide effective strategies for both domain generalization and domain adaptation in visual recognition.