\begin{abstract}
Multi-label image classification requires understanding not only the visual features of individual objects but also their semantic relationships within a scene. 
To address this challenge, we propose \textbf{KGGAT} --  \textbf{[K]}nowledge-\textbf{[G]}uided \textbf{[G]}raph \textbf{[At]}tention Network that integrates external knowledge graphs with attention-based visual reasoning. 
Unlike conventional transformer-based models that rely solely on visual correlations, \texttt{KGGAT} incorporates structured semantic priors from \texttt{ConceptNet55} and \texttt{OpenAI}-derived knowledge graphs to explicitly model inter-label dependencies. 
A dual-path architecture couples a Graph Attention Network (\texttt{GAT}) for label reasoning with a transformer-based visual encoder, enabling cross-modal alignment between visual and semantic spaces. 
In addition, an element-wise addition fusion strategy and an Asymmetric Loss (\texttt{ASL}) objective enhance feature interaction and effectively mitigate class imbalance. 
Extensive experiments on \texttt{MS-COCO} and \texttt{VOC 2007} demonstrate that KGGAT achieves state-of-the-art performance, attaining \textbf{89.21\% mAP} and \textbf{96.85\% mAP}, respectively. 
Qualitative analyses further show that \texttt{KGGAT} provides accurate object localization and interpretable label reasoning across diverse scenes, validating its effectiveness in knowledge-guided multi-label image classification. 
Code and pretrained models will be released to facilitate future research on knowledge-guided visual understanding.
\end{abstract}

\begin{figure}
\centering
\includegraphics[width=0.5\textwidth]{KGGAT3.png}

\caption{
Overall architecture of \texttt{KGGAT}. 
The model integrates transformer-based visual reasoning with a knowledge-aware refinement module to generate label-specific attention maps and interpretable multi-label predictions (best viewed in color).
}
    \label{fig:KGGCN1}
\end{figure}



