\section{Related Work}
\label{sec:relatedwork}
\subsection{Multi-Label Image Classification}
Multi-label image classification (\texttt{MLIC}) aims to predict all object categories that simultaneously appear in an image, making it more challenging than single-label recognition due to complex semantic dependencies and label co-occurrences. 
Early \texttt{MLIC} approaches relied on \emph{global feature pooling} from convolutional neural networks (\texttt{CNN}), using classifiers such as sigmoid cross-entropy~\cite{wang2016cnn, zhang2018multilabel}. 
While simple, these models ignore spatial context and inter-label relationships. 
Subsequent works explored region-based reasoning~\cite{wei2015hcp}, recurrent label dependency modeling~\cite{wang2016cnn, chen2019multi}, and attention mechanisms~\cite{wang2017multi, you2020cross} to capture label correlations. 
However, most \texttt{CNN}-based methods struggle to model long-range semantic dependencies or adaptively localize label-relevant regions.

Graph-based methods model semantic dependencies by representing each label as a node and their correlations as edges derived from dataset statistics or external priors~\cite{chen2019multi,bai2021multi}. 
While \texttt{GCN}-based approaches capture structured relationships, their static and dataset-specific adjacency limits generalization to unseen label combinations. 
In addition, isotropic message propagation in standard \texttt{GCN} fails to model asymmetric or higher-order label dependencies.

\subsection{Knowledge-Guided Learning}
To overcome the limitations of purely data-driven reasoning, several studies have incorporated external knowledge sources into visual recognition systems~\cite{marino2017more, kampffmeyer2019rethinking, wang2021enhancing}. 
Knowledge-guided learning aims to inject human-level semantic priors to improve generalization and interpretability. 
Knowledge graphs (\texttt{KG}) such as \texttt{WordNet}~\cite{miller1995wordnet}, \texttt{ConceptNet55}~\cite{speer2017conceptnet}, and \texttt{CSKG}~\cite{ilievski2021cskg} provide structured relations among object concepts, enabling reasoning beyond co-occurrence statistics. 
For example,~\cite{chen2019multi} and~\cite{wang2020multi} utilize \texttt{GCN} to propagate information along fixed \texttt{KG} edges, while~\cite{zhu2022knowledge} leverage relation embeddings to guide attention in scene understanding. 
However, existing knowledge-guided \texttt{GCN} remain \emph{static} -- the same adjacency prior is applied to all images regardless of content -- leading to limited flexibility. 
Recent surveys~\cite{kgmlsurvey2024, kgcv2022} highlight the need for dynamic knowledge integration that adapts semantic reasoning to image context.

\subsection{Transformer-Based Multi-Label Classification}
Transformer-based architectures have recently achieved state-of-the-art results in \texttt{MLIC} by reformulating label dependencies as query-based attention interactions~\cite{liu2021query2label, ridnik2021mldecoder, cho2022CoFormer}. 
{Query2Label}~\cite{liu2021query2label} first introduced the idea of treating each label as a query in a Transformer decoder, allowing label-specific attention to image features. 
\texttt{CoFormer}~\cite{cho2022CoFormer} enhanced this framework through contextual co-occurrence learning, while {ML-Decoder}~\cite{ridnik2021mldecoder} achieved scalable label-space modeling with sparse decoding. 
Despite their success, these models rely purely on visual correlations without incorporating structured external knowledge, leading to redundancy and limited interpretability in complex scenes.