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Updated: Feb 19, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Learning Compact Semantic Information and Reliable Pseudo-Labels for Incomplete Multi-View Multi-Label

Yadong Liu, Chengliang Liu, Jie Wen

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    Summary
    This summary is machine-generated.

    This study introduces CTRL, a framework for incomplete multi-view multi-label classification. CTRL effectively handles missing data by learning condensed representations and using evidential neural networks for uncertainty estimation, improving classification accuracy and reliability.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Multi-view data, including multi-feature, multi-sequence, and multi-modal data, is prevalent in various domains.
    • Multi-view multi-label classification aims to improve classification by utilizing information from multiple data perspectives.
    • Incomplete data, characterized by missing views and labels, presents a significant challenge in practical multi-view multi-label classification tasks.

    Purpose of the Study:

    • To propose a novel framework, CTRL, for incomplete multi-view multi-label classification that addresses challenges posed by partially missing views and labels.
    • To develop a method for learning condensed representations that capture essential shared semantic information across incomplete views.
    • To integrate uncertainty estimation for improved label classification and pseudo-label generation.

    Main Methods:

    • CTRL framework utilizes a novel objective loss function to enhance shared cross-view semantic information and suppress redundant intra-view information.
    • Joint representation learning is employed to extract task-relevant features even from incomplete views.
    • Beta Evidential Neural Network integrated with Dempster-Shafer theory is used for label distribution modeling and uncertainty estimation.

    Main Results:

    • The proposed CTRL framework demonstrates superior performance on benchmark datasets.
    • CTRL exhibits enhanced accuracy, robustness, and reliability in multi-view multi-label classification tasks with incomplete data.
    • The use of estimated uncertainty and belief mass for generating high-reliability pseudo-labels further boosts model performance.

    Conclusions:

    • CTRL provides an effective solution for multi-view multi-label classification with missing views and labels.
    • The framework successfully extracts task-relevant representations from incomplete multi-view data.
    • CTRL offers a reliable approach for uncertainty estimation and pseudo-label generation, leading to improved classification outcomes.