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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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Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition.

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    This study introduces a knowledge-guided graph routing (KGGR) framework for multi-label image recognition. KGGR effectively uses label co-occurrence knowledge to improve recognition accuracy, especially for categories with limited data.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Multi-label image recognition (MLR) faces challenges in capturing complex label dependencies and co-occurrences.
    • Existing methods using recurrent neural networks (RNNs) or long short-term memory (LSTM) fail to fully exploit mutual interactions between semantic regions and labels.
    • Current approaches often require extensive training data and struggle to generalize to novel categories with few samples.

    Purpose of the Study:

    • To propose a novel knowledge-guided graph routing (KGGR) framework that integrates prior statistical label correlation knowledge with deep neural networks.
    • To enhance multi-label image recognition by guiding adaptive information propagation among categories, reducing reliance on large training datasets.
    • To improve generalization capabilities for novel categories with limited training samples in multi-label few-shot learning (ML-FSL).

    Main Methods:

    • Constructing a structured knowledge graph based on statistical label co-occurrence to represent label correlations.
    • Initializing the graph with label semantics to learn semantic-specific features and using graph propagation networks for contextualized feature representation.
    • Initializing graph nodes with classifier weights and employing another propagation network to transfer messages, leveraging correlated label information for classifier training.

    Main Results:

    • The KGGR framework demonstrates superior performance on traditional multi-label image recognition (MLR) tasks.
    • Significant improvements were observed in multi-label few-shot learning (ML-FSL) scenarios, particularly for categories with limited training samples.
    • The proposed method outperforms current state-of-the-art approaches on public benchmarks by considerable margins.

    Conclusions:

    • The knowledge-guided graph routing (KGGR) framework effectively unifies prior knowledge with deep learning for improved multi-label image recognition.
    • KGGR successfully addresses the limitations of existing methods by explicitly modeling label co-occurrences and facilitating adaptive information propagation.
    • The framework shows strong potential for enhancing MLR and ML-FSL tasks, offering better generalization and reduced data dependency.