% \section{Conclusion}
% \label{sec:conclusion}

% In this work, we introduced \texttt{KGGAT}, a Knowledge-Guided Graph Attention Network for multi-label image classification that integrates visual reasoning with external semantic knowledge. 
% By combining transformer-based label queries with an image-conditioned graph attention mechanism and a knowledge-consistency regularization, \texttt{KGGAT} effectively captures both contextual dependencies and commonsense relations among labels. 
% Extensive experiments on \texttt{MS-COCO} and \texttt{VOC 2007} demonstrate that \texttt{KGGAT} consistently outperforms existing transformer and graph-based approaches, achieving superior accuracy and interpretability with minimal architectural overhead.

% In future work, we will extend \texttt{KGGAT} to large-scale vision–language understanding by integrating textual knowledge and image captions, and explore dynamic knowledge graph construction from large language models to enable adaptive, data-driven semantic reasoning.


\section{Conclusion}
\label{sec:conclusion}

We presented \texttt{KGGAT}, a knowledge-guided graph attention network for multi-label image classification. By combining transformer-based label queries with image-conditioned graph attention and knowledge-consistency regularization, \texttt{KGGAT} captures both contextual and semantic label relations. Experiments on \texttt{MS-COCO} and \texttt{VOC 2007} show that it outperforms existing transformer- and graph-based methods with minimal overhead. These results highlight the potential of knowledge-guided attention as an effective and practical direction for robust multi-label recognition. Future work will extend \texttt{KGGAT} toward vision--language understanding through textual knowledge and dynamic knowledge graph construction.


\section*{Acknowledgements}
This research is supported by Faculty of Sci Eng \& Built Env, Deakin University, VIC 3220, Australia. 