A multi-label image classification method via graph attention network with dynamic and static label correlations

Key Points

• Improved accuracy in multi-label classification using a graph attention network, enhancing label correlation handling.
• Performance significantly improved with dynamic label correlations over static ones, ensuring better relationships between labels.
• Observational analysis across diverse image datasets supports the efficacy of the proposed method in classifying complex images.
• Findings imply that leveraging both dynamic and static correlations can lead to more robust image classification systems.