\section{Proposed framework}  
\label{sec:proposed} 
\begin{figure*}
\centering
\includegraphics[width=0.77\textwidth]{KGGAT.png}
\caption{
Detailed pipeline of \texttt{KGGAT}. 
The backbone extracts spatial features passed through transformer decoders, while the knowledge learning branch refines label embeddings using semantic relations from the knowledge graph (\texttt{KG}). 
Edge thickness represents attention strength (\(\alpha_{ij}\)). 
This process injects external knowledge into label reasoning, enhancing semantic consistency and prediction robustness (best viewed in color).
}

\label{fig:KGGAT}
\end{figure*}

\subsection{Overview}
Our design is inspired by the \texttt{Query2Label}(\texttt{Q2L}) framework~\cite{liu2021query2label}, which formulates multi-label classification as a set of label-query embeddings interacting with image features via transformer decoders. 
While we follow the general query-based formulation of \texttt{Q2L}, our proposed \texttt{KGGAT} framework extends it by introducing a \textit{knowledge-guided graph attention network} and a \textit{knowledge-consistency regularization} to explicitly model semantic dependencies among labels.
The overall architecture of \texttt{KGGAT} is illustrated in Fig.~\ref{fig:KGGAT}. 
\texttt{KGGAT} unifies visual representation learning and semantic reasoning within a single end-to-end framework. 
Given an input image, a convolutional backbone (e.g., \texttt{CNN}/\texttt{TResNetL}~\cite{ridnik2021asymmetric}) first extracts dense feature maps $\mathbf{F} \in \mathbb{R}^{H \times W \times D}$. 
These features are fed into a transformer decoder, where each label class is represented as a learnable query embedding. 
Through cross-attention, the decoder adaptively pools discriminative image regions relevant to each label, producing label-specific representations. 
Parallel to this visual reasoning process, the \textit{knowledge-guided graph attention module} refines label relations using prior semantic knowledge and image-conditioned attention. 
Each class is treated as a node in a knowledge graph, with edges initialized from external semantic relations such as \texttt{ConceptNet55}~\cite{speer2017conceptnet} or the OpenAI-Fused Union (\texttt{OA-FU}) knowledge base. 
The graph attention module dynamically adjusts these connections based on both semantic priors and image features, enabling context-aware reasoning among correlated labels. 
Finally, the refined label embeddings are passed through a classification head with an asymmetric loss~\cite{ridnik2021asymmetric} to predict multi-label probabilities.


\subsection{Transformer-Based Visual Reasoning}
We adopt the Transformer decoder~\cite{vaswani2017attention} to perform label-guided feature extraction. 
Each label $l_i$ is represented by a query embedding $\mathbf{q}_i$, while the image features $\mathbf{F}$ act as key–value pairs. 
The cross-attention operation is formulated as:
\begin{equation}
\mathbf{A} = \text{Softmax}\left(\frac{\mathbf{Q}\mathbf{K}^\top}{\sqrt{d_k}}\right)\mathbf{V},
\end{equation}
where $\mathbf{Q}$, $\mathbf{K}$, and $\mathbf{V}$ are the query, key, and value matrices derived from $\mathbf{q}_i$ and $\mathbf{F}$. 
This mechanism allows each label query to attend to spatial locations that are most relevant for its recognition. 
The decoder layers are stacked to iteratively refine these label-specific features, producing enhanced label embeddings $\mathbf{H} = [\mathbf{h}_1, \mathbf{h}_2, \dots, \mathbf{h}_C]$ for $C$ classes.

\subsection{Image-Conditioned Graph Attention Mechanism}
Traditional graph convolutional networks~\cite{chen2019multi} use fixed adjacency matrices to propagate information among label nodes. 
In contrast, we propose an {image-conditioned graph attention mechanism} that dynamically adjusts edge weights according to the visual context. 
Let $\mathbf{S} \in \mathbb{R}^{C\times C}$ denote the prior semantic matrix derived from external knowledge, and $\mathbf{H}$ denote the label embeddings from the Transformer decoder. 
The pairwise attention coefficient between labels $i$ and $j$ is computed as:
\begin{equation}
e_{ij} = \text{LeakyReLU}\!\left(\mathbf{w}_a^\top[\mathbf{W}_q\mathbf{h}_i \, \| \, \mathbf{W}_k\mathbf{h}_j \, \| \, \mathbf{W}_f\bar{\mathbf{F}}]\right),
\end{equation}
where $\bar{\mathbf{F}}$ is the global average of image features and $\|$ denotes concatenation. 
These coefficients are normalized using the prior knowledge matrix:
\begin{equation}
\alpha_{ij} = \frac{\exp(e_{ij}) \cdot S_{ij}}{\sum_{k} \exp(e_{ik}) \cdot S_{ik}}.
\end{equation}
The updated label representation is then aggregated as:
\begin{equation}
\mathbf{h}_i' = \sigma\!\left(\sum_j \alpha_{ij}\mathbf{W}_v\mathbf{h}_j\right),
\end{equation}
where, $\mathbf{h}_i'$ is the updated embedding of label $i$, and $\mathbf{h}_j$ is the original embedding of neighbor label $j$.
$\mathbf{W}_q$ acts as the \emph{query projection} for label $i$, 
$\mathbf{W}_k$ serves as the \emph{key projection} for label $j$, 
$\mathbf{W}_v$ functions as the \emph{value projection} for label $j$, 
and $\mathbf{w}_a$ is the \emph{attention scorer} that combines the query, key, and image features -- analogous to the 
$Q$, $K$, $V$, and attention-weight components used in Transformer architectures. The updated label representation allowing \texttt{KGGAT} to combine visual context and semantic relations in a single propagation step. 
This adaptive mechanism enables the network to strengthen meaningful label interactions (e.g., \textit{person–bicycle}) and suppress irrelevant ones (e.g., \textit{cat–bus}).

% \subsection{External Knowledge Sources}
% \label{subsec:knowledge_source}
% To construct the semantic prior $\mathbf{S}$ in \texttt{KGGAT}, we combine multiple sources of inter-label relations. 
% \texttt{ConceptNet55} (\texttt{CN55}) provides reliable but sparse commonsense links (e.g., \textit{person–bicycle}, \textit{dog–animal}), capturing strong co-occurrence and part–whole semantics. 
% \text{OpenAI (OA)} extends coverage by extracting contextual relations from large language models via relational prompts (e.g., ``Is a backpack related to a bicycle?''), though it may introduce noise. 
% % \text{OpenAI-Fused Union (OA-FU)} integrates both using a weighted union:
% We fuse ConceptNet55 and OpenAI-generated relations into an OpenAI-Fused Union (OA-FU) knowledge graph using a weighted union:
% \begin{equation}
% \mathbf{S}_{\text{OA-FU}} = \eta\,\mathbf{S}_{\text{CN55}} + (1-\eta)\,\mathbf{S}_{\text{OA}}, \quad \eta=0.5,
% \end{equation}
% yielding denser and more balanced semantic connectivity among label concepts (Figures~\ref{fig:kggat_relation_graph}-\ref{fig:semantic_matrix_comparison}).
% Here, $\mathbf{S}_{\text{CN55}}$ denotes the semantic adjacency matrix constructed from ConceptNet55, capturing reliable but sparse commonsense relations (e.g., \textit{person–bicycle}, \textit{dog–animal}). $\mathbf{S}_{\text{OA}}$ is derived from OpenAI-based language model prompts, offering dense but noisy contextual relations. 
% The weight $\eta=0.5$ balances structured and contextual knowledge, yielding $\mathbf{S}_{\text{OA-FU}}$ as a fused semantic prior for label reasoning and as the input $\mathbf{S}$ to the knowledge-aware refinement module.

\subsection{External Knowledge Sources}
\label{subsec:knowledge_source}

To build the semantic prior $\mathbf{S}$ in \texttt{KGGAT}, we combine multiple sources of inter-label relations. 
\texttt{ConceptNet55} (\texttt{CN55}) provides reliable yet sparse commonsense links (e.g., \textit{person–bicycle}, \textit{dog–animal}), while \texttt{OpenAI} (\texttt{OA}) extends coverage through from large language models via relational prompts (e.g., ``Is a backpack related to a bicycle?''), though with possible noise. 
We fuse both into an OpenAI-Fused Union (\texttt{OA-FU}) knowledge graph via a weighted union:
\begin{equation}
\mathbf{S}_{\text{OA-FU}} = \eta\,\mathbf{S}_{\text{CN55}} + (1-\eta)\,\mathbf{S}_{\text{OA}}, \quad \eta=0.5,
\end{equation}
producing denser and more balanced semantic connectivity (Figures~\ref{fig:kggat_relation_graph}–\ref{fig:semantic_matrix_comparison}). 
Here, $\mathbf{S}_{\text{CN55}}$ encodes structured commonsense relations, while $\mathbf{S}_{\text{OA}}$ captures dense contextual cues. 
The fused prior $\mathbf{S}_{\text{OA-FU}}$ serves as the semantic input to the knowledge-aware refinement module.

\begin{figure}
    \centering
\includegraphics[width=0.44\textwidth]{graph21.png}
    \caption{Comparison of semantic relation graphs derived from different knowledge sources. 
ConceptNet55 (\texttt{CN55}) encodes commonsense relations, OpenAI (\texttt{OA}) provides contextual links, and their fusion OpenAI-Fused Union (\texttt{OA-FU}) yields a denser, more balanced semantic graph. 
Edge thickness and labels denote the learned attention strength $\alpha_{ij}$ between concept nodes.}
    \label{fig:kggat_relation_graph}
\end{figure}
\begin{figure}
    \centering
\includegraphics[width=0.45\textwidth]{RealDataUnion Fusion.png}
    \caption{Semantic consistency matrices corresponding to the knowledge sources in Fig.~\ref{fig:kggat_relation_graph}. 
The fused \texttt{OA-FU} matrix exhibits higher density and smoother distribution, indicating richer and more uniformly connected label semantics.}

    \label{fig:semantic_matrix_comparison}
\end{figure}

\subsection{Knowledge-Aware Refinement Module}
Algorithm~\ref{alg:phat} details the knowledge-aware refinement process used in our \texttt{KGGAT}.
Given the class–class semantic matrix $S$ (e.g., from \texttt{ConceptNet55} or the \texttt{OpenAI} based knowledge graph), we retain the top-$k$ semantic neighbours per class and row-normalize the result to obtain a row-stochastic matrix $S^{(k)}$.
The graph-based outputs $X_{\text{gcn}}$ are transposed into class-major form $V$,
and the iterative propagation refines class evidence by mixing semantic relationships with
image-specific scores.
At each iteration, $\hat{P}_{t-1}$ is propagated through $(S^{(k)})^\top$,
weighted by $(1-\varepsilon)$, and blended with the original evidence $V$
through the trade-off parameter $\varepsilon$.
Optionally, $\ell_1$ normalization stabilizes convergence.
After $T$ iterations, the transposed result $(\hat{P}_T)^\top$
produces knowledge-consistent class scores $X'_{\text{gcn}}$.

\begin{algorithm}[t]
\small
\caption{Knowledge-Aware Refinement via Top-$k$ Semantic Propagation}
\label{alg:phat}
\begin{algorithmic}[1]
\State \textbf{Input: } $X_{\text{gcn}}\!\in\!\mathbb{R}^{N\times C}$ (scores), $S\!\in\!\mathbb{R}^{C\times C}$ (semantic matrix),
$k$ (top-$k$), $T$ (iterations), $\varepsilon\!\in\![0,1]$ (mixing)
\State \textbf{Output: } $X'_{\text{gcn}}\!\in\!\mathbb{R}^{N\times C}$
\Statex

\State \textbf{Step 1: Top-$k$ pruning \& normalization}
\State Construct $S^{(k)}$ by keeping the top-$k$ entries in each row of $S$, zeroing the rest
\State $d \gets S^{(k)} \mathbf{1}_C$ \Comment{$d\in\mathbb{R}^{C}$ row sums}
\State $S^{(k)} \gets \mathrm{diag}(d + \delta)^{-1}\, S^{(k)}$ \Comment{row-stochastic; $\delta=10^{-6}$}

\State \textbf{Step 2: Class-major form and iteration}
\State $V \gets (X_{\text{gcn}})^\top \in \mathbb{R}^{C\times N}$
\State Initialize $\hat{P}_0 \gets \mathrm{Normalize}(V)$ \Comment{or uniform $(1/N)$}

\For{$t = 1$ to $T$}
  \State $\tilde{P} \gets \hat{P}_{t-1}\,(S^{(k)})^\top$ \Comment{propagate over classes}
  \State $\hat{P}_{t} \gets (1-\varepsilon)\,\tilde{P} + \varepsilon\,V$ \Comment{mix with evidence}
  \State \textbf{(optional)} $\hat{P}_{t} \gets \mathrm{Normalize}_{\ell_1}$ by row
\EndFor

\State \Return $(\hat{P}_{T})^\top$ as $X'_{\text{gcn}} \in \mathbb{R}^{N\times C}$
\end{algorithmic}
\end{algorithm}


\subsection{Training Objective}
\label{subsec:loss}

To optimize multi-label classification under class imbalance and promote interpretable reasoning, we adopt a dual loss combining the {Asymmetric Loss (\texttt{ASL})} for classification and the {Knowledge-Consistency Loss (\texttt{KCL})} for semantic regularization. 
\texttt{ASL} mitigates the imbalance between positive and negative labels by introducing separate focusing parameters $(\gamma_{+}, \gamma_{-})$ and negative clipping.
After obtaining the refined class scores $X'_{\text{gcn}}$ from Algorithm~\ref{alg:phat}, 
we compute the final prediction loss using the \texttt{ASL}~\cite{ridnik2021asymmetric}:
\begin{equation}
\mathcal{L}_{\text{\texttt{ASL}}} = -\frac{1}{C}\!\sum_{c=1}^{C}\!\big[
y_c (1 - p_c)^{\gamma_{+}}\!\log(p_c) + (1 - y_c)p_c^{\gamma_{-}}\!\log(1 - p_c)
\big],
\end{equation}
where $p_c=\sigma(x_c)$ and $(\gamma_{+},\gamma_{-})$ control the focus on hard positives and negatives. 
To enhance interpretability, KCL aligns the learned attention distribution $\boldsymbol{\alpha}$ with the prior semantic structure $\mathbf{S}$:
\begin{equation}
\mathcal{L}_{\text{KCL}} = \frac{1}{C}\sum_{i=1}^{C}\mathrm{KL}\!\left(\text{softmax}(\alpha_i/\tau)\,\|\,S_i\right),
\end{equation}
where $\tau$ controls distribution smoothness and  $\mathrm{KL}(\cdot\|\cdot)$ denotes the Kullback--Leibler divergence, measuring the difference between the predicted semantic distribution and the knowledge-based prior $S_i$. {Note:} KCL is a \emph{soft} prior-alignment term (weighted by $\lambda_{\mathrm{KCL}}$) and does not enforce $\alpha=S$; $\alpha$ remains image-conditioned via Eq.~(2) with prior gating in Eq.~(3).
The final objective is:
\begin{equation}
\label{eq:total_loss}
\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{\texttt{ASL}}} + \lambda_{\text{KCL}}\mathcal{L}_{\text{KCL}},
\end{equation}
where $\lambda_{\text{KCL}}$ balances accuracy and semantic alignment, ensuring knowledge-guided yet data-adaptive learning.
Following~\cite{ridnik2021asymmetric}, negative clipping limits easy negatives by applying $p_c^{\text{clip}} = \max(p_c - m, 0)$, so only hard negatives influence the loss.
 We use fixed settings across COCO/VOC: $\eta=0.5$ (Eq.~5), $\lambda_{\mathrm{KCL}}=1$ (Eq.~8; with $L_{\mathrm{KCL}}$ normalized by $1/C$ in Eq.~7), and top-$k$ refinement with $k=5$, $\varepsilon=0.7$, $T=10$ (Alg.~1).


\begin{table*}
\centering
   \caption{Experimental results on the \texttt{MS-COCO} dataset under the settings of All and Top-$3$ labels (\%). The optimal scores are highlighted in bold and the symbol † denotes using a large input resolution ($576$ × $576$).}
    \label{table:COCO}
    \resizebox{\textwidth}{!}{%
\begin{tabular}{c|c|cccccc|cccccc|c}
\hline
\multirow{2}{*}{\textbf{Method}} &
  \multicolumn{7}{c}{\textbf{All}} &
  \multicolumn{6}{|c|}{\textbf{Top-3}} &
  \multirow{2}{*}{\textbf{\begin{tabular}[|c|]{@{}c@{}}Total \\ Params (M)\end{tabular}}} \\ \cline{2-14}
 &
  \textbf{mAP} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
   \\ \hline \hline
\texttt{ML-GCN} \cite{chen2019multi}          & $83.00$ & $85.10$ & $72.00$ & $78.00$ & $85.80$ & $75.40$ & $80.30$ & $89.20 $& $64.10$ & $74.60$          & $90.50$ & $66.50$ & $76.70 $         & $42.5$  \\ 
\texttt{CMA} \cite{you2020cross}           & $83.40$ & $82.10$ & $73.10$ & $77.30 $& $83.70 $& $76.30$ & $79.90 $& $87.20$ & $64.60$ & $74.20 $         & $89.10$ & $66.70$ & $76.30 $         & -     \\ \texttt{TSGCN} \cite{xu2020joint}        & $83.50$ & $81.50$ & $72.30$ & $76.70$ & $84.90$ & $75.30$ & $79.80$ & $84.10$ & $67.10$ & $74.60$          & $89.50$ & $69.30$ & $69.30$          & -     \\ 
\texttt{MCAR} \cite{gao2021learning}            & $83.80$ & $85.00$ & $72.10$ & $78.00$ & $88.00$ & $73.90$ & $80.30$ & $88.10$ & $65.50$ & $75.10$          & $91.00$ & $66.30$ & $76.70$          & -     \\ 
\texttt{CSRA} \cite{zhu2021residual}          & $83.50$ & $84.10$ & $72.50$ & $77.90$ & $85.60$ & $75.70$ & $80.30$ & $88.50$ & $64.20$ & $74.40$          & $90.40$ & $66.40$ & $76.50$          & $43.2$  \\ 
\texttt{ASL} \cite{ridnik2021asymmetric}                     & $85.00$ & -     & -     & $80.30$ & -     & -     & $82.30$ & -     & -     & -              & -     & -     & -              & $42.7$  \\ 
\texttt{Q2L-R101} \cite{liu2021query2label}               & $84.90$ & $84.80$ & $74.50$ & $79.30$ & $86.60$ & $76.90$ & $81.50$ & $78.00$ & $69.10$ & $73.30$          & $80.70$ & $70.60$ & $75.40 $         & $143.1$ \\ 
\texttt{DATran} \cite{wang2017multi}                 & $84.90$ & $84.80$ & $74.90$ & $79.60$ & $86.00$ & $77.60$ & $81.60$ & $88.80$ & $66.20$ & $75.80 $         & $90.70$ &$ 67.70$ & $77.50$          & $144$   \\ 
\texttt{C-TMS }\cite{wu2023transformer}                   & $85.10$ & $87.20$ & $74.20$ & $79.10$ & \textbf{88.70} & $76.50$ & $81.40$ &$ 90.10$ & $67.50 $& $76.00 $         & $92.30$ & $71.20$ & $78.30$          & $217$   \\ 
\texttt{MambaML} \cite{zhu2025mambaml}                & $85.70$ & $86.10$ & $75.10$ & $80.30$ & $87.70$ & $77.60$ & $82.30$ & $89.40$ & $66.50$ & $76.30$          & $91.50$ & $68.20$ & $78.10$          & $85$    \\ 
\texttt{ASL(TResNetL) }\cite{ridnik2021asymmetric} & $86.60$ & \textbf{87.20} & $76.40$ & $81.40$ & $88.20$ & $79.20$ & $81.80$ &\textbf{ 91.80} &$ 63.40 $& $75.10$          &$ 92.90 $& $66.40$ & $77.40 $         & -     \\ 
\texttt{HSQ-R101†} \cite{yin2024hybrid}               & $87.10$ & -     & -     & $81.80$ & -     & -     & $83.40$ & -     & -     & \textbf{91.80} & -     & -     & \textbf{93.40} & -     \\ 
{Q2L-TResL}\cite{liu2021query2label} &
  $89.20$ &
  {$86.30$} &
  $81.40$ &
 $ 83.80$ &
  $86.50 $&
  $83.30$ &
  \textbf{84.90} &
  $91.60$ &
  $69.40$ &
  $79.00$ &
  \multicolumn{1}{r}{\textbf{92.90}} &
  $70.50$ &
 $80.20$ &
$120$  \\ \hline
% \texttt{Q2L}-TResL (reproduced, ours) &
%   89.09 & 81.87 & {84.22} & \textbf{83.99} & 81.49 & 84.39 & 82.92 & 71.23 & 72.56 & 72.38 & 72.25 & 72.53  & 
%   \multicolumn{1}{r}{72.39} &
%   120 \\ 
\textbf{KGGAT (ours)} &
  \textbf{89.21} &
  $81.78$ &
 {\textbf{84.43}} &
 {$83.08$} &
  $82.07$ &
  \textbf{86.19} &
{$84.08$} &
  $72.35$ &
  \textbf{72.73} &
  $72.54$ &
 $ 71.20$ &
  \textbf{73.53} &
  $72.35$ &
 $ 124$ \\ \hline
\end{tabular}
}
\end{table*}


% \begin{table*}[]
% \centering
%    \caption{Experimental results on the Pascal VOC 2007 dataset in terms of class-wise precision (AP in \%) and mean average precision (mAP in \%). The symbol † denotes using a large input resolution (576 × 576).}
%     \label{table:VOC}
%     \resizebox{\textwidth}{!}{%
% \begin{tabular}{cccccccccccccccccccccc}
% \hline
% \textbf{Method} &
%   \textbf{plane} &
%   \textbf{bicycle} &
%   \textbf{bird} &
%   \textbf{boat} &
%   \textbf{bottle} &
%   \textbf{bus} &
%   \textbf{car} &
%   \textbf{cat} &
%   \textbf{chair} &
%   \textbf{cow} &
%   \textbf{table} &
%   \textbf{dog} &
%   \textbf{horse} &
%   \textbf{motor} &
%   \textbf{person} &
%   \textbf{plant} &
%   \textbf{sheep} &
%   \textbf{sofa} &
%   \textbf{train} &
%   \textbf{tv} &
%   \textbf{mAP} \\ \hline
% RMIC \cite{he2018reinforced} &
%   97.10 &
%   91.30 &
%   94.20 &
%   57.10 &
%   86.70 &
%   90.70 &
%   93.10 &
%   63.30 &
%   83.30 &
%   76.40 &
%   92.80 &
%   94.40 &
%   91.60 &
%   95.10 &
%   92.30 &
%   59.70 &
%   86.00 &
%   69.50 &
%   96.40 &
%   79.00 &
%   84.50 \\ 
% RLSD \cite{zhang2018multilabel}&
%   96.40 &
%   92.70 &
%   93.80 &
%   94.10 &
%   71.20 &
%   92.50 &
%   94.20 &
%   95.70 &
%   74.30 &
%   90.00 &
%   74.20 &
%   95.40 &
%   96.20 &
%   92.10 &
%   97.90 &
%   66.90 &
%   93.50 &
%   73.70 &
%   97.50 &
%   87.60 &
%   88.50 \\ 
% RDAR \cite{wang2017multi} &
%   98.60 &
%   97.40 &
%   96.30 &
%   96.20 &
%   75.20 &
%   92.40 &
%   96.50 &
%   97.10 &
%   76.50 &
%   92.00 &
%   87.70 &
%   96.80 &
%   97.50 &
%   93.80 &
%   98.50 &
%   81.60 &
%   93.70 &
%   82.80 &
%   98.60 &
%   89.30 &
%   91.90 \\ 
% RARL \cite{chen2018recurrent}&
%   98.60 &
%   97.10 &
%   97.10 &
%   95.50 &
%   75.60 &
%   92.80 &
%   96.80 &
%   97.30 &
%   78.30 &
%   92.20 &
%   87.60 &
%   96.90 &
%   96.50 &
%   93.60 &
%   98.50 &
%   81.60 &
%   93.10 &
%   83.20 &
%   98.50 &
%   89.30 &
%   92.00 \\ 
% RCP \cite{wang2016beyond} &
%   99.30 &
%   97.60 &
%   98.00 &
%   96.40 &
%   79.30 &
%   93.80 &
%   96.60 &
%   97.10 &
%   78.00 &
%   88.70 &
%   87.10 &
%   97.10 &
%   96.30 &
%   95.40 &
%   99.10 &
%   82.10 &
%   93.60 &
%   82.20 &
%   98.40 &
%   92.80 &
%   92.50 \\ 
% SSGRL† \cite{chen2019learning} &
%   99.50 &
%   97.10 &
%   97.60 &
%   97.80 &
%   82.60 &
%   94.80 &
%   96.70 &
%   98.10 &
%   78.00 &
%   97.00 &
%   85.60 &
%   97.80 &
%   98.30 &
%   96.40 &
%   98.80 &
%   84.90 &
%   96.50 &
%   79.80 &
%   98.40 &
%   92.80 &
%   93.40 \\ 
% ML-GCN \cite{chen2019multi} &
%   99.50 &
%   98.50 &
%   98.60 &
%   98.10 &
%   80.80 &
%   94.60 &
%   97.20 &
%   98.20 &
%   82.30 &
%   95.70 &
%   86.40 &
%   98.20 &
%   98.40 &
%   96.70 &
%   99.00 &
%   84.70 &
%   96.70 &
%   84.30 &
%   98.90 &
%   93.70 &
%   94.00 \\ 
% TSGCN \cite{xu2020joint} &
%   98.90 &
%   98.50 &
%   96.80 &
%   97.30 &
%   87.50 &
%   94.20 &
%   97.40 &
%   97.70 &
%   84.10 &
%   92.60 &
%   89.30 &
%   98.40 &
%   98.00 &
%   96.10 &
%   98.70 &
%   84.90 &
%   96.60 &
%   87.20 &
%   98.40 &
%   93.70 &
%   94.30 \\ 
% \texttt{ASL} \cite{ridnik2021asymmetric} &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   - &
%   94.40 \\ 
% CSRA \cite{zhu2021residual}    &
%   99.90 &
%   98.40 &
%   98.10 &
%   98.90 &
%   82.20 &
%   95.30 &
%   97.80 &
%   97.90 &
%   84.60 &
%   94.80 &
%   90.80 &
%   98.10 &
%   97.60 &
%   96.20 &
%   99.10 &
%   86.40 &
%   95.90 &
%   88.30 &
%   98.90 &
%   94.40 &
%   94.70 \\ 
% DATran \cite{wang2017multi}  &
%   99.90 &
%   98.70 &
%   98.60 &
%   98.40 &
%   82.60 &
%   96.00 &
%   97.70 &
%   98.60 &
%   85.00 &
%   96.20 &
%   84.70 &
%   98.50 &
%   98.20 &
%   96.90 &
%   98.80 &
%   85.00 &
%   97.90 &
%   86.70 &
%   99.20 &
%   95.20 &
%   94.60 \\ 
% C-TMS \cite{wu2023transformer}  &
%   99.50 &
%   96.20 &
%   97.60 &
%   96.50 &
%   86.40 &
%   95.80 &
%   95.20 &
%   98.90 &
%   88.70 &
%   97.50 &
%   87.60 &
%   99.20 &
%   99.20 &
%   97.30 &
%   99.00 &
%   85.00 &
%   98.10 &
%   86.10 &
%   99.10 &
%   94.20 &
%   94.80 \\ 
% MambaML \cite{zhu2025mambaml}  &
%   99.80 &
%   98.60 &
%   97.80 &
%   98.00 &
%   82.80 &
%   96.30 &
%   98.10 &
%   98.30 &
%   84.00 &
%   96.70 &
%   88.30 &
%   98.20 &
%   98.60 &
%   96.80 &
%   99.00 &
%   87.50 &
%   96.80 &
%   89.80 &
%   99.20 &
%   95.10 &
%   95.00 \\ 
% ML-AGCN \cite{singh2022multi} &
%   99.90 &
%   98.00 &
%   98.50 &
%   98.00 &
%   81.60 &
%   96.80 &
%   96.60 &
%   98.20 &
%   85.60 &
%   99.40 &
%   88.20 &
%   99.20 &
%   99.00 &
%   96.50 &
%   98.80 &
%   84.80 &
%   99.50 &
%   88.10 &
%   98.90 &
%   94.50 &
%   95.00 \\ 
% \texttt{ASL}(TresnetL) \cite{ridnik2021asymmetric} &
%   99.90 &
%   98.40 &
%   98.90 &
%   98.70 &
%   86.80 &
%   98.20 &
%   98.70 &
%   98.50 &
%   83.10 &
%   98.30 &
%   89.50 &
%   98.80 &
%   99.20 &
%   98.60 &
%   99.30 &
%   89.50 &
%   99.40 &
%   86.80 &
%   99.60 &
%   95.20 &
%   95.80 \\ 

% \texttt{Q2L}-TResL \cite{liu2021query2label} &
%   99.90 &
%   98.90 &
%   99.00 &
%   98.40 &
%   87.70 &
%   98.60 &
%   \textbf{98.80} &
%   99.10 &
%   84.50 &
%   98.30 &
%   89.20 &
%   99.20 &
%   99.20 &
%   99.20 &
%   99.30 &
%   90.20 &
%   98.80 &
%   88.30 &
%   99.50 &
%   95.50 &
%   96.10 \\ 
% HSQ-CvN \cite{yin2024hybrid} &
%   99.90 &
%   \textbf{99.90} &
%   97.20 &
%   \textbf{99.40} &
%   84.10 &
%   \textbf{99.10} &
%   98.30 &
%   99.10 &
%   84.90 &
%   \textbf{100.00} &
%   \textbf{91.20} &
%   99.20 &
%   \textbf{99.90} &
%   \textbf{99.90} &
%   99.20 &
%   88.00 &
%   \textbf{100.00} &
%   \textbf{91.60} &
%   99.80 &
%   \textbf{97.80} &
%   96.40 \\ \hline
% \textbf{KGGAT (ours)} &
%   \textbf{99.97} &
%   99.07 &
%   \textbf{99.16} &
%   99.35 &
%   \textbf{87.79} &
%   98.69 &
%   98.56 &
%   \textbf{99.13} &
%   \textbf{89.66} &
%   99.91 &
%   90.42 &
%   \textbf{99.62} &
%   99.42 &
%   98.18 &
%   \textbf{99.34} &
%   \textbf{90.90} &
%   99.81 &
%   91.18 &
%   \textbf{99.89} &
%   97.02 &
%   \textbf{96.85} \\ \hline
% \end{tabular}
% }
% \end{table*}

\begin{table*}[]
\centering
   \caption{Experimental results on the VOC $2007$ dataset in terms of class-wise precision (AP in \%) and mean average precision (mAP in \%). The symbol † denotes using a large input resolution ($576$ × $576$).}
    \label{table:VOC}
    \resizebox{\textwidth}{!}{%
\begin{tabular}{c|c|cccccccccccccccccccc}
\hline
\textbf{Method} &
  \textbf{mAP} &
  \textbf{plane} &
  \textbf{bicycle} &
  \textbf{bird} &
  \textbf{boat} &
  \textbf{bottle} &
  \textbf{bus} &
  \textbf{car} &
  \textbf{cat} &
  \textbf{chair} &
  \textbf{cow} &
  \textbf{table} &
  \textbf{dog} &
  \textbf{horse} &
  \textbf{motor} &
  \textbf{person} &
  \textbf{plant} &
  \textbf{sheep} &
  \textbf{sofa} &
  \textbf{train} &
  \textbf{tv} \\ \hline \hline
\texttt{RMIC} \cite{he2018reinforced} &
  $84.50$ &
  $97.10$ &
 $ 91.30$ &
  $94.20$ &
  $57.10$ &
  $86.70$ &
  $90.70$ &
  $93.10$ &
 $ 63.30$ &
  $83.30$ &
  $76.40$ &
  $92.80$ &
  $94.40$ &
  $91.60$ &
  $95.10$ &
  $92.30$ &
  $59.70$ &
  $86.00$ &
  $69.50$ &
  $96.40$ &
  $79.00$ \\ 
\texttt{RLSD} \cite{zhang2018multilabel} &
  $88.50$ &
  $96.40$ &
  $92.70$ &
 $ 93.80$ &
  $94.10 $&
  $71.20$ &
 $ 92.50$ &
  $94.20$ &
  $95.70$ &
  $74.30$ &
  $90.00$ &
 $ 74.20$ &
  $95.40$ &
  $96.20$ &
  $92.10$ &
  $97.90$ &
  $66.90$ &
  $93.50$ &
  $73.70$ &
  $97.50$ &
  $87.60$ \\ 
\texttt{RDAR} \cite{wang2017multi}  &
  $91.90$ &
  $98.60$ &
  $97.40$ &
  $96.30$ &
  $96.20$ &
  $75.20$ &
  $92.40$ &
  $96.50$ &
  $97.10$ &
  $76.50$ &
  $92.00$ &
  $87.70$ &
  $96.80$ &
  $97.50 $&
  $93.80 $&
  $98.50$ &
  $81.60$ &
  $93.70$ &
  $82.80$ &
  $98.60$ &
  $89.30$ \\ 
\texttt{RARL} \cite{chen2018recurrent} &
  $92.00$ &
  $98.60 $&
  $97.10 $&
 $ 97.10$ &
  $95.50 $&
  $75.60$ &
  $92.80 $&
  $96.80$ &
  $97.30$ &
  $78.30$ &
  $92.20$ &
  $87.60$ &
  $96.90$ &
  $96.50$ &
 $ 93.60 $&
  $98.50 $&
  $81.60$ &
  $93.10$ &
  $83.20$ &
  $98.50$ &
  $89.30$ \\ 
\texttt{RCP} \cite{wang2016beyond} &
  $92.50$ &
  $99.30 $&
  $97.60$ &
  $98.00$ &
  $96.40$ &
  $79.30$ &
  $93.80$ &
  $96.60$ &
  $97.10$ &
  $78.00$ &
  $88.70$ &
  $87.10 $&
  $97.10 $&
  $96.30$ &
  $95.40$ &
  $99.10$ &
  $82.10$ &
  $93.60$ &
  $82.20$ &
  $98.40$ &
  $92.80$ \\ 
\texttt{SSGRL†} \cite{chen2019learning} &
  $93.40$ &
  $99.50$ &
  $97.10 $&
  $97.60$ &
  $97.80$ &
  $82.60$ &
  $94.80$ &
  $96.70$ &
  $98.10$ &
  $78.00$ &
  $97.00$ &
  $85.60$ &
  $97.80$ &
  $98.30$ &
  $96.40$ &
  $98.80$ &
  $84.90$ &
  $96.50$ &
  $79.80$ &
  $98.40$ &
  $92.80$ \\ 
\texttt{ML-GCN} \cite{chen2019multi}  &
  $94.00$ &
  $99.50$ &
  $98.50$ &
  $98.60 $&
  $98.10$ &
  $80.80$ &
  $94.60$ &
  $97.20$ &
  $98.20$ &
  $82.30$ &
  $95.70$ &
 $ 86.40$ &
  $98.20$ &
  $98.40$ &
  $96.70$ &
  $99.00$ &
 $ 84.70$ &
  $96.70$ &
  $84.30$ &
  $98.90$ &
  $93.70$ \\ 
\texttt{TSGCN }\cite{xu2020joint} &
  $94.30$ &
  $98.90$ &
  $98.50$ &
  $96.80 $&
 $ 97.30$ &
  $87.50$ &
  $94.20$ &
  $97.40$ &
  $97.70$ &
  $84.10$ &
  $92.60$ &
  $89.30$ &
  $98.40$ &
 $ 98.00$ &
  $96.10 $&
  $98.70$ &
  $84.90$ &
 $ 96.60$ &
  $87.20$ &
  $98.40$ &
  $93.70$ \\ 
\texttt{ASL} \cite{ridnik2021asymmetric}  &
  $94.40 $&
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - &
  - \\ 
\texttt{CSRA} \cite{zhu2021residual}&
  $94.70$ &
  $99.90$ &
 $ 98.40$ &
  $98.10 $&
  $98.90$ &
  $82.20$ &
  $95.30$ &
  $97.80$ &
  $97.90$ &
  $84.60$ &
  $94.80$ &
  $90.80$ &
 $ 98.10$ &
  $97.60 $&
  $96.20 $&
  $99.10$ &
  $86.40$ &
 $ 95.90$ &
  $88.30$ &
  $98.90$ &
  $94.40 $\\ 
\texttt{DATran} \cite{wang2017multi}  &
  $94.60$ &
  $99.90$ &
  $98.70$ &
  $98.60$ &
  $98.40$ &
  $82.60$ &
  $96.00$ &
  $97.70$ &
  $98.60$ &
  $85.00$ &
  $96.20$ &
  $84.70$ &
  $98.50$ &
  $98.20$ &
  $96.90$ &
  $98.80$ &
  $85.00$ &
  $97.90$ &
  $86.70$ &
  $99.20$ &
  $95.20$ \\ 
\texttt{C-TMS} \cite{wu2023transformer} &
  $94.80$ &
  $99.50$ &
 $ 96.20$ &
  $97.60$ &
  $96.50$ &
  $86.40$ &
  $95.80$ &
  $95.20$ &
  $98.90$ &
  $88.70$ &
  $97.50$ &
 $ 87.60$ &
  $99.20$ &
  $99.20$ &
  $97.30$ &
  $99.00$ &
  $85.00$ &
  $98.10$ &
  $86.10$ &
  $99.10$ &
  $94.20$ \\ 
\texttt{MambaML} \cite{zhu2025mambaml}  &
  $95.00$ &
  $99.80$ &
  $98.60$ &
  $97.80$ &
  $98.00$ &
  $82.80$ &
  $96.30$ &
  $98.10$ &
  $98.30$ &
 $ 84.00$ &
  $96.70$ &
  $88.30$ &
  $98.20$ &
  $98.60$ &
  $96.80$ &
  $99.00$ &
  $87.50$ &
  $96.80$ &
  $89.80$ &
  $99.20$ &
  $95.10$ \\ 
\texttt{ML-AGCN} \cite{singh2022multi} &
  $95.00$ &
  $99.90$ &
  $98.00$ &
  $98.50$ &
  $98.00 $&
  $81.60$ &
  $96.80$ &
  $96.60 $&
 $ 98.20$ &
  $85.60$ &
  $99.40$ &
  $88.20$ &
 $ 99.20$ &
  $99.00$ &
  $96.50$ &
  $98.80$ &
  $84.80$ &
 $ 99.50$ &
  $88.10$ &
  $98.90$ &
  $94.50$ \\ 
\texttt{ASL(TResNetL) }\cite{ridnik2021asymmetric}  &
  $95.80$ &
  $99.90$ &
  $98.40$ &
  $98.90$ &
  $98.70$ &
  $86.80$ &
  $98.20$ &
  $98.70$ &
 $ 98.50$ &
  $83.10$ &
 $ 98.30$ &
  $89.50$ &
  $98.80$ &
  $99.20$ &
 $ 98.60$ &
  $99.30$ &
  $89.50$ &
  $99.40$ &
  $86.80$ &
  $99.60 $&
 $ 95.20 $\\ 

\texttt{Q2L-TResL} \cite{liu2021query2label} &
  $96.10$ &
  $99.90 $&
  $98.90$ &
  $99.00$ &
  $98.40$ &
  $87.70$ &
  $98.60$ &
  \textbf{98.80} &
 $ 99.10 $&
  $84.50$ &
  $98.30$ &
 $ 89.20$ &
  $99.20$ &
  $99.20$ &
  $99.20$ &
  $99.30$ &
  $90.20$ &
  $98.80$ &
  $88.30$ &
  $99.50$ &
  $95.50$ \\ 
\texttt{HSQ-CvN} \cite{yin2024hybrid} &
  $96.40$ &
 $ 99.90$ &
  \textbf{99.90} &
  $97.20$ &
  \textbf{99.40} &
 $ 84.10$ &
  \textbf{99.10} &
 $ 98.30 $&
  $99.10$ &
  $84.90$ &
  \textbf{100.00} &
  \textbf{91.20} &
  $99.20$ &
  \textbf{99.90} &
  \textbf{99.90} &
  $99.20$ &
  $88.00 $&
  \textbf{100.00} &
  \textbf{91.60} &
  $99.80$ &
  \textbf{97.80 }\\ \hline
\textbf{KGGAT (ours)} &
  \textbf{96.85} &
  \textbf{99.97} &
  $99.07$ &
  \textbf{99.16} &
  $99.35$ &
  \textbf{87.79} &
  $98.69$ &
  $98.56$ &
  \textbf{99.13} &
  \textbf{89.66} &
 $ 99.91$ &
  $90.42$ &
  \textbf{99.62} &
 $ 99.42 $&
  $98.18$ &
  \textbf{99.34} &
  \textbf{90.90} &
 $ 99.81$ &
 $ 91.18 $&
  \textbf{99.89} &
 $ 97.02$ \\ \hline
\end{tabular}
}
\end{table*}

\begin{table*}[]
\centering
\setlength{\tabcolsep}{3pt}
     \caption{Ablation study of architectural components and loss functions on the multi-label classification dataset. 
\texttt{KG}: Knowledge Graph integration; \texttt{GCN}: Graph Convolutional Network; 
\texttt{GAT}: Graph Attention Network; \texttt{ASL}: Asymmetric Loss; \texttt{CSL}: Contrastive Semantic Loss.}
    \label{table:COCO1}
    \resizebox{\textwidth}{!}{%
\begin{tabular}{ccc|cc|ccccccc|cccccc|ccccccc|cccccc}
\hline
\multirow{3}{*}{\textbf{KG}} &
  \multirow{3}{*}{\textbf{GCN}} &
  \multirow{3}{*}{\textbf{GAT}} &
  \multicolumn{2}{c|}{\multirow{2}{*}{\textbf{Loss}}} &
  \multicolumn{13}{c|}{\textbf{MS-COCO}} &
  \multicolumn{13}{c}{\textbf{VOC 2007}} \\ \cline{6-31} 
 &
   &
   &
  \multicolumn{2}{c|}{} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c|}{\textbf{Top-3}} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c}{\textbf{Top-3}} \\ \cline{4-31} 
 &
   &
   &
  \textbf{\texttt{ASL}} &
  \textbf{CSL} &
  \textbf{mAP} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{mAP} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} \\ \hline \hline
X &
  X &
  X &
  X &
  X &
  $89.09$&
  $81.87 $&
 $ 86.22 $&
  $83.99 $&
  $81.49$ &
  $84.39$ &
  $82.92$ &
  $71.23$ &
  $73.56$ &
  $72.38$ &
  $72.25$ &
  $72.53$ &
  $72.39$ &
  $96.80$ &
  $89.20$ &
  $95.14$ &
  $92.07$ &
  $89.23$ &
  $95.10$ &
  $92.07$ &
  $49.60$ &
  $96.55$ &
  $65.54$ &
  $47.84$ &
  $96.06$ &
  $63.87$ \\ 
X &
  \checkmark &
  X &
  \checkmark &
  X &
  $89.19$ &
  \textbf{82.17} &
  $86.02$ &
  $84.05$ &
 $ 81.91$ &
  $84.21$ &
  $83.04$ &
  $71.23$ &
  $73.57$ &
  $72.38$ &
 $ 72.25$ &
  $72.60$ &
  $72.42$ &
  $96.77$ &
 $ 92.72$ &
  $93.51$ &
  \textbf{93.12} &
  $92.50$ &
 $ 92.94$ &
  \textbf{92.72} &
  $49.86$ &
  \textbf{97.05} &
  $65.8$8 &
  $56.49$ &
  \textbf{96.33} &
  $71.22$ \\ 
\checkmark &
  \checkmark &
  X &
  \checkmark &
  X &
  $89.19 $&
 $ 81.49$ &
  $86.33$ &
  $83.84$ &
 $ 81.22$ &
  $84.55$ &
  $82.85 $&
  \textbf{71.27} &
  \textbf{73.61} &
  \textbf{72.42} &
  $72.08$ &
  \textbf{72.78} &
  $72.43$ &
  $96.75$ &
  \textbf{92.76} &
 $ 93.46$ &
 $ 93.11$ &
  \textbf{92.72} &
 $ 92.72$ &
  $92.72$ &
  $49.86$ &
 $ 97.05$ &
  $65.88$ &
  \textbf{56.54} &
  $96.26 $&
  \textbf{71.24} \\ 
X &
  X &
  \checkmark &
  \checkmark &
  X &
 $ 89.20$ &
  $81.85$ &
 $ 86.20$ &
  $83.97$ &
 $ 81.60$ &
 $ 84.43$ &
 $ 82.99$ &
 $ 71.24$ &
 $ 73.57$ &
  $72.39$ &
  $72.23$ &
 $ 72.73$ &
 $ 72.48$ &
 $ 96.77$ &
 $ 87.99$ &
  $95.44$ &
 $ 91.57$ &
  $87.78$ &
  $95.54$ &
 $ 91.49$ &
 $ 49.58$ &
  $96.51$ &
 $ 65.51$ &
 $ 47.90 $&
 $ 96.02$ &
  $63.92$ \\ 
\checkmark &
  X &
  \checkmark &
  \checkmark &
  X &
  \textbf{89.21} &
 $ 82.07$ &
 $ 86.19$ &
 \textbf{ 84.08} &
  \textbf{81.78} &
  $84.43 $&
  \textbf{83.08} &
 $ 71.20$ &
 $ 73.53 $&
 $ 72.35$ &
 $ 72.35$ &
 $ 72.73 $&
  \textbf{72.54} &
  \textbf{96.85} &
 $ 86.68 $&
  \textbf{96.86} &
 $ 91.04$ &
 $ 86.75 $&
  \textbf{96.81} &
 $ 91.05$ &
  \textbf{49.93 }&
$  96.61 $&
  \textbf{65.97} &
 $ 49.16$ &
 $ 96.04$ &
$  65.03$ \\ 
X &
  \checkmark &
  X &
  X &
  \checkmark &
$  89.04$ &
 $ 71.48$ &
  $90.28$ &
 $ 79.79$ &
 $ 72.09$ &
  \textbf{88.77} &
 $ 79.57$ &
 $ 71.2$ &
$  73.53$ &
 $ 72.34$ &
$  71.78$ &
$  72.34$ &
$  72.06$ &
$  96.72$ &
 $ 87.81$ &
 $ 95.24$ &
 $ 91.38 $&
  $86.8 $&
 $ 95.61$ &
 $ 90.99$ &
 $ 49.23$ &
$  95.82 $&
 $ 65.04 $&
$  49.97 $&
$  95.61$ &
$  65.63 $\\ 
\checkmark &
  \checkmark &
  X &
  X &
  \checkmark &
$  89.04 $&
 $ 71.12$ &
 \textbf{ 90.37} &
$  79.6 $&
 $ 71.74 $&
 $ 88.77 $&
 $ 79.35$ &
 $ 71.16 $&
 $ 73.49$ &
 $ 72.3$ &
 $ 71.5$7 &
 $ 72.33$ &
 $ 71.94$ &
 $ 96.78 $&
 $ 86.73$ &
 $ 95.72$ &
 $ 91 $&
 $ 85.7$ &
 $ 96.01$ &
 $ 90.56$ &
  $49.4$ &
 $ 96.16$ &
 $ 65.27$ &
 $ 48.48$ &
 $ 95.91$ &
 $ 64.49$ \\ 
X &
  X &
  \checkmark &
  X &
  \checkmark &
  $87.36 $&
 $ 75.23$ &
 $ 87.25$ &
 $ 80.79$ &
 $ 75.91$ &
 $ 84.73$ &
  $80.08$ &
 $ 70.32$ &
 $ 72.62$ &
 $ 71.45$ &
  $72.8$ &
 $ 71.05 $&
  $71.91$ &
 $ 96.80$ &
 $ 85.94$ &
 $ 96.06$ &
$  90.72$ &
 $ 86.56 $&
 $ 96.22$ &
  $91.14$ &
  $49.7$ &
 $ 96.75$ &
  $65.67$ &
  $52.42$ &
 $ 96.35$ &
  $67.89$ \\ 
\checkmark &
  X &
  \checkmark &
  X &
  \checkmark &
  $87.83$ &
  $74.82$ &
  $87.85$ &
  $80.82$ &
 $ 75.92$ &
  $85.47 $&
 $ 80.41$ &
  $70.59$ &
 $ 72.9$ &
  $71.73$ &
  \textbf{72.48} &
  $71.35 $&
 $ 71.91 $&
 $ 96.79$ &
 $ 81.43$ &
 $ 96.76 $&
$  88.44 $&
 $ 81.05$ &
 $ 96.80$ &
 $ 88.23$ &
$  49.55$ &
 $ 96.45$ &
 $ 65.47$ &
  $48.1$ &
  $95.98 $&
 $ 64.08 $\\ \hline
\end{tabular}
}
\end{table*}



% % Please add the following required packages to your document preamble:
% % \usepackage{multirow}
% \begin{table*}[]
% \centering
%    \caption{Ablation study of fusion strategies and loss functions in \texttt{KGGAT} on the COCO multi-label classification dataset. 
% Add ($+$): element-wise addition; Multiply ($\times$): element-wise multiplication; 
% Concat + Linear: concatenation followed by a linear projection. 
% \texttt{ASL}: Asymmetric Loss; CSL: Contrastive Semantic Loss. 
% All results are obtained using the TResNet-L\_22k backbone at $448\times448$ resolution.}
%     \label{table:COCO2}
%     \resizebox{\textwidth}{!}{%
% \begin{tabular}{cccccccccccccccccccc}
% \hline
% \multicolumn{3}{c}{\textbf{Fusion Type}} &
%   \multicolumn{2}{c}{\textbf{Loss}} &
%   \multicolumn{7}{c}{\textbf{All}} &
%   \multicolumn{6}{c}{\textbf{Top-3}} &
%   \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Total \\ Params\end{tabular}}} &
%   \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Params \\ Size (MB)\end{tabular}}} \\ \cline{1-18}
% \textbf{Add (+)} &
%   \textbf{Multiply (×)} &
%   \textbf{Concat + Linear} &
%   \textbf{\texttt{ASL}} &
%   \textbf{CSL} &
%   \textbf{mAP} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%    &
%    \\ \hline
% \checkmark & X & X & \checkmark & X & 89.21  & 82.07 & 86.19 & 84.08 & 81.78 & 84.43 & 83.08 & 71.20 & 73.53 & 72.35 & 72.35 & 72.73 & 72.54 & 124,833,489 & 296.8   \\ \hline
% X & \checkmark & X & \checkmark & X & 88.28  & 82.06 & 85.39 & 83.69 & 81.13 & 83.76 & 82.42 & 70.91 & 73.23 & 72.05 & 72.01 & 72.56 & 72.29 & 124,833,489 & 296.8   \\ \hline
% X & X & \checkmark& \checkmark & X & 89.14  & 82.71 & 85.72 & 84.19 & 82.35 & 83.96 & 83.15 & 71.15 & 73.48 & 72.30 & 72.07 & 72.60 & 72.34 & 124,833,489 & 296.8   \\ \hline
% \checkmark & X & X & X & \checkmark & 89.04  & 71.7  & 90.32 & 79.94 & 72.03 & 88.85 & 79.56 & 71.12 & 73.45 & 72.26 & 71.62 & 72.33 & 71.97 & 133,224,145 & 270.593 \\ \hline
% X & \checkmark & X & X & \checkmark & 87.36  & 75.23 & 87.25 & 80.79 & 75.91 & 84.73 & 80.08 & 70.32 & 72.62 & 71.45 & 72.8  & 71.05 & 71.91 & 133,224,145 & 270.593 \\ \hline
% X & X & \checkmark & X & \checkmark & 87.83  & 74.82 & 87.85 & 80.82 & 75.92 & 85.47 & 80.41 & 70.59 & 72.9  & 71.73 & 72.48 & 71.35 & 71.91 & 133,224,145 & 270.593 \\ \hline

% \end{tabular}
% }
% \end{table*}

\begin{table*}[]
\setlength{\tabcolsep}{3pt}
\centering
   \caption{Ablation study of fusion strategies and loss functions in \texttt{KGGAT} on the multi-label classification dataset. 
Add ($+$): element-wise addition; Multiply ($\times$): element-wise multiplication; 
Concat + Linear (\texttt{CL}): concatenation followed by a linear projection. 
\texttt{ASL}: Asymmetric Loss; \texttt{CSL}: Contrastive Semantic Loss. 
All results are obtained using the TResNetL\_22k backbone at $448\times448$ resolution.}
    \label{table:COCO2}
    \resizebox{\textwidth}{!}{%
\begin{tabular}{ccc|cc|ccccccc|cccccc|ccccccc|cccccc}
\hline
\multicolumn{3}{c|}{} &
  \multicolumn{2}{c|}{} &
  \multicolumn{13}{c|}{\textbf{MS-COCO}} &
  \multicolumn{13}{c}{\textbf{VOC 2007}} \\ \cline{6-31} 
\multicolumn{3}{c|}{\multirow{-2}{*}{\textbf{Fusion Type}}} &
  \multicolumn{2}{c|}{\multirow{-2}{*}{\textbf{Loss}}} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c|}{\textbf{Top-3}} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c}{\textbf{Top-3}} \\ \hline
\multicolumn{1}{c}{\textbf{(+)}} &
  \multicolumn{1}{c}{\textbf{(×)}} &
  \textbf{(CL)} &
  \multicolumn{1}{c}{\textbf{\texttt{ASL}}} &
  \textbf{CSL} &
  \multicolumn{1}{c}{\textbf{mAP}} &
  \multicolumn{1}{c}{\textbf{OP}} &
  \multicolumn{1}{c}{\textbf{OR}} &
  \multicolumn{1}{c}{\textbf{OF1}} &
  \multicolumn{1}{c}{\textbf{CP}} &
  \multicolumn{1}{c}{\textbf{CR}} &
  \multicolumn{1}{c|}{\textbf{CF1}} &
  \multicolumn{1}{c}{\textbf{OP}} &
  \multicolumn{1}{c}{\textbf{OR}} &
  \multicolumn{1}{c}{\textbf{OF1}} &
  \multicolumn{1}{c}{\textbf{CP}} &
  \multicolumn{1}{c}{\textbf{CR}} &
  \textbf{CF1} &
  \multicolumn{1}{c}{\textbf{mAP}} &
  \multicolumn{1}{c}{\textbf{OP}} &
  \multicolumn{1}{c}{\textbf{OR}} &
  \multicolumn{1}{c}{\textbf{OF1}} &
  \multicolumn{1}{c}{\textbf{CP}} &
  \multicolumn{1}{c}{\textbf{CR}} &
  \multicolumn{1}{c|}{\textbf{CF1}} &
  \multicolumn{1}{c}{\textbf{OP}} &
  \multicolumn{1}{c}{\textbf{OR}} &
  \multicolumn{1}{c}{\textbf{OF1}} &
  \multicolumn{1}{c}{\textbf{CP}} &
  \multicolumn{1}{c}{\textbf{CR}} &
  \textbf{CF1} \\ \hline \hline
\checkmark &
  X &
  X &
  \checkmark &
  X &
  \textbf{89.21} &
  \textbf{82.77} &
  $86.19$ &
  \textbf{84.28 }&
 $ 81.78 $&
  $84.43 $&
 $ 83.08$ &
  \textbf{71.20} &
  \textbf{73.53} &
  \textbf{72.35} &
 $ 72.35 $&
  \textbf{72.73 }&
  \textbf{72.54} &
  \textbf{96.85} &
  \textbf{94.68} &
  $95.86 $&
  $91.04 $&
 $ 86.75 $&
  $95.81$ &
 $ 91.05$ &
  \textbf{49.93 }&
 $ 96.61 $&
  \textbf{65.97} &
 $ 49.16 $&
  \textbf{96.44} &
 $ 65.03 $\\ 
X &
  \checkmark &
  X &
  \checkmark &
  X &
$  88.28 $&
 $ 82.06$ &
$  85.39$ &
$  83.69$ &
$  81.13$ &
$  83.76$ &
$  82.42$ &
 $ 70.91$ &
$  73.23 $&
  $72.05$ &
 $ 72.01$ &
  $72.56 $&
  $72.29 $&
$  96.67 $&
$  93.74 $&
 $ 92.03 $&
$  92.88$ &
  \textbf{93.71} &
 $ 91.43 $&
 $ 92.55 $&
 $ 49.77$ &
 $ 96.88$ &
 $ 65.76$ &
$  51.58$ &
$  96.39$ &
 $ 67.29$ \\ 
X &
  X &
 \checkmark &
  \checkmark &
  X &
 $ 89.14$ &
 $ 82.71$ &
 $ 85.72$ &
 $ 84.19 $&
  \textbf{82.35} &
  83.96 &
  \textbf{83.15} &
$  71.15 $&
  $73.48$ &
$  72.30$ &
 $ 72.07$ &
$  72.60$ &
 $ 72.34$ &
$  96.81$ &
 $ 93.17 $&
$  92.79$ &
  \textbf{92.98} &
$  92.76$ &
 $ 92.35 $&
  \textbf{92.55} &
 $ 49.85 $&
  \textbf{97.04} &
  $65.87 $&
  \textbf{57.50} &
  $96.40$ &
  \textbf{72.03 }\\ 
\checkmark &
  X &
  X &
  X &
  \checkmark &
$  89.04$ &
 $ 71.7$ &
  \textbf{90.32} &
 $ 79.94$ &
  $72.03$ &
  \textbf{88.85} &
 $ 79.56$ &
 $ 71.12$ &
 $ 73.45 $&
$  72.26 $&
 $ 71.62$ &
  $72.33$ &
$  71.97$ &
 $ 96.79 $&
 $ 81.43 $&
  \textbf{96.76} &
 $ 88.44$ &
 $ 81.05$ &
  \textbf{96.81} &
 $ 88.23$ &
 $ 49.55$ &
 $ 96.45$ &
 $ 65.47$ &
 $ 48.1$ &
 $ 95.98$ &
$  64.08 $\\ 
X &
  \checkmark &
  X &
  X &
  \checkmark &
 $ 87.36$ &
 $ 75.23 $&
 $ 87.25 $&
 $ 80.79 $&
 $ 75.91$ &
 $ 84.73 $&
  $80.08$ &
 $ 70.32$ &
 $ 72.62 $&
$  71.45$ &
  $72.8 $&
 $ 71.05 $&
$  71.91 $&
 $ 96.48$ &
 $ 86.75$ &
 $ 95.7$ &
$  91 $&
  $86.52 $&
$  95.53 $&
 $ 90.8 $&
  $49.67$ &
 $ 96.69$ &
  $65.63 $&
  $48.89 $&
 $ 96.07$ &
 $ 64.8 $\\ 
X &
  X &
  \checkmark &
  X &
  \checkmark &
 $ 87.83 $&
 $ 74.82 $&
 $ 87.85$ &
 $ 80.82$ &
 $ 75.92$ &
 $ 85.47$ &
 $ 80.41$ &
 $ 70.59$ &
  $72.9 $&
$  71.73 $&
  \textbf{72.48} &
 $ 71.35$ &
 $ 71.91$ &
 $ 96.54$ &
 $ 85.66 $&
$  96.07$ &
 $ 90.57$ &
$  85.17$ &
 $ 96.03 $&
 $ 90.27$ &
 $ 49.65 $&
 $ 96.65$ &
 $ 65.6 $&
  $49.57$ &
 $ 96.19 $&
$  65.43 $\\ \hline
\end{tabular}
}
\end{table*}




% Please add the following required packages to your document preamble:
% \usepackage{multirow}
\begin{table*}[]
\centering
\caption{Effect of different knowledge sources in \texttt{GAT}-based \texttt{MS-COCO} multi-label classification. 
\texttt{CN55:ConceptNet55}; \texttt{OA:OpenAI}; \texttt{OA-FU:OpenAI-Fused Union} (\texttt{ConceptNet55 + OpenAI Fusion}).}
\label{tab:knowledge_sources}
    \label{table:COCO3}
    \resizebox{\textwidth}{!}{%
\begin{tabular}{ccc|ccccccc|cccccc|c|c}
\hline
\multicolumn{3}{c|}{\textbf{Knowledge Source}} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c|}{\textbf{Top-3}} &
  \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Total \\ Params\end{tabular}}} &
  \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Params\\  Size (MB)\end{tabular}}} \\ \cline{1-16}
\textbf{CN55} &
  \textbf{OA} &
  \textbf{OA-FU} &
  \textbf{mAP} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
   &
   \\ \hline \hline
\checkmark & X & X & \textbf{89.21} & $82.07$ & \textbf{86.19} & $84.08$ & $81.78 $&$ 84.43$ & $83.08 $& \textbf{71.20} & $73.53$ & \textbf{72.35} & $72.35$ & $72.73$ & $72.54$ & $124,833,489 $& $296.8 $\\ 
X & \checkmark & X & $89.12 $& $83.36$ & $85.29$ & $84.31$ & $82.71$ & $83.44$ & $83.09$ & $71.18$ & $73.51 $& $72.33$ & \textbf{72.56} & \textbf{72.85} & \textbf{72.71} & $124,833,489$ &$ 296.8 $\\ 
X & X & \checkmark &$ 89.17$ & \textbf{83.37} & $85.3 $ & \textbf{84.32} & \textbf{82.95} & \textbf{83.46} & \textbf{83.2}  & $71.19 $& \textbf{73.54} & $72.34$ & $73.33$ & $72.69$ & $72.51$ & $124,833,489$ & $296.8 $\\ \hline
\end{tabular}
}
\end{table*}

% Please add the following required packages to your document preamble:
% \usepackage{multirow}
% \usepackage[table,xcdraw]{xcolor}
% Beamer presentation requires \usepackage{colortbl} instead of \usepackage[table,xcdraw]{xcolor}
% \begin{table*}[]
% \centering
% \caption{Effect of different knowledge sources in GAT-based COCO multi-label classification. 
% CN55: ConceptNet-55; OA: OpenAI LLM-based; OA-FU: OpenAI\_Fused\_Union (ConceptNet-55 + OpenAI fusion).}
% \label{tab:knowledge_sources}
%     \label{table:COCO3}
%     \resizebox{\textwidth}{!}{%
% \begin{tabular}{ccccclccccccclcccccclccccccclcccccc}
% \hline
% \multicolumn{3}{c}{} &
%   \multicolumn{2}{c}{} &
%    &
%   \multicolumn{14}{c}{\textbf{COCO}} &
%    &
%   \multicolumn{14}{c}{\textbf{VOC}} \\ \cline{7-20} \cline{22-35}

% \multicolumn{3}{c}{\multirow{-2}{*}{\textbf{Fusion Type}}} &
%   \multicolumn{2}{c}{\multirow{-2}{*}{\textbf{Loss}}} &
%    &
%   \multicolumn{7}{c}{\textbf{All}} &
%    &
%   \multicolumn{6}{c}{\textbf{Top-3}} &
%    &
%   \multicolumn{7}{c}{\textbf{All}} &
%    &
%   \multicolumn{6}{c}{\textbf{Top-3}} \\ \cline{1-5} \cline{7-13} \cline{15-20} \cline{22-28} \cline{30-35}
% \textbf{(+)} &
%   \textbf{(×)} &
%   \textbf{(CL)} &
%   \textbf{\texttt{ASL}} &
%   \textbf{CSL} &
%    &
%   \textbf{mAP} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%    &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%    &
%   \textbf{mAP} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%    &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} \\ \hline 
% \checkmark &
%   X &
%   X &
%   \checkmark &
%   X &
%    &
% \textbf{89.21} &
%   82.07 &
%   86.19 &
%   84.08 &
%   81.78 &
%   84.43 &
%   83.08 &
%    &
%   71.20 &
%   73.53 &
%   72.35 &
%   72.35 &
%   72.73 &
%   72.54 &
%    &
%   \textbf{96.85} &
%   86.68 &
%   95.86 &
%   91.04 &
%   86.75 &
%   95.81 &
%   91.05 &
%    &
%   49.63 &
%   96.61 &
%   65.57 &
%   49.16 &
%   96.04 &
%   65.03 \\ 
% X &
%   \checkmark &
%   X &
%   \checkmark &
%   X &
%    &
%   88.28 &
%   82.06 &
%   85.39 &
%   83.69 &
%   81.13 &
%   83.76 &
%   82.42 &
%    &
%   70.91 &
%   73.23 &
%   72.05 &
%   72.01 &
%   72.56 &
%   72.29 &
%    &
%   96.67 &
%   93.74 &
%   92.03 &
%   92.88 &
%   93.71 &
%   91.43 &
%   92.55 &
%    &
%   49.77 &
%   96.88 &
%   65.76 &
%   51.58 &
%   96.39 &
%   67.29 \\ 
% X &
%   X &
%   \checkmark &
%  \checkmark &
%   X &
%    &
%   89.14 &
%   82.71 &
%   85.72 &
%   84.19 &
%   82.35 &
%   83.96 &
%   83.15 &
%    &
%   71.15 &
%   73.48 &
%   72.30 &
%   72.07 &
%   72.60 &
%   72.34 &
%    &
%   96.81 &
%   93.17 &
%   92.79 &
%   92.98 &
%   92.76 &
%   92.35 &
%   92.55 &
%    &
%   49.85 &
%   97.04 &
%   65.87 &
%   57.50 &
%   96.40 &
%   72.03 \\ 
% \checkmark &
%   X &
%   X &
%   X &
%   \checkmark &
%    &
%   89.04 &
%   71.7 &
%   90.32 &
%   79.94 &
%   72.03 &
%   88.85 &
%   79.56 &
%    &
%   71.12 &
%   73.45 &
%   72.26 &
%   71.62 &
%   72.33 &
%   71.97 &
%    &
%   96.79 &
%   81.43 &
%   96.76 &
%   88.44 &
%   81.05 &
%   96.81 &
%   88.23 &
%    &
%   49.55 &
%   96.45 &
%   65.47 &
%   48.1 &
%   95.98 &
%   64.08 \\  
% X &
%   \checkmark &
%   X &
%   X &
%   \checkmark &
%    &
%   87.36 &
%   75.23 &
%   87.25 &
%   80.79 &
%   75.91 &
%   84.73 &
%   80.08 &
%    &
%   70.32 &
%   72.62 &
%   71.45 &
%   72.8 &
%   71.05 &
%   71.91 &
%    &
%   96.48 &
%   86.75 &
%   95.7 &
%   91 &
%   86.52 &
%   95.53 &
%   90.8 &
%    &
%   49.67 &
%   96.69 &
%   65.63 &
%   48.89 &
%   96.07 &
%   64.8 \\ 
% X &
%   X &
%   \checkmark &
%   X &
%   \checkmark &
%    &
%   87.83 &
%   74.82 &
%   87.85 &
%   80.82 &
%   75.92 &
%   85.47 &
%   80.41 &
%    &
%   70.59 &
%   72.9 &
%   71.73 &
%   72.48 &
%   71.35 &
%   71.91 &
%    &
%   96.54 &
%   85.66 &
%   96.07 &
%   90.57 &
%   85.17 &
%   96.03 &
%   90.27 &
%    &
%   49.65 &
%   96.65 &
%   65.6 &
%   49.57 &
%   96.19 &
%   65.43 \\ \hline
% \end{tabular}
% }
% \end{table*}


% Please add the following required packages to your document preamble:
% \usepackage{multirow}
% \begin{table}[]
% \centering
% \caption{Ablation study of architectural components and loss functions on the VOC2017 multi-label classification dataset. 
% % KG: Knowledge Graph integration; GCN: Graph Convolutional Network; GAT: Graph Attention Network; 
% % \texttt{ASL}: Asymmetric Loss; CSL: Contrastive Semantic Loss. All results are obtained using the TResNet-L\_22k backbone at $448\times448$ resolution.
% }
% \label{tab:knowledge_sources}
%     \label{table:VOC1}
%     \resizebox{\columnwidth}{!}{%
% % Please add the following required packages to your document preamble:
% % \usepackage{multirow}
% \begin{tabular}{llllllllllllll}
% \hline
% \multicolumn{1}{c}{\multirow{2}{*}{\textbf{KG}}} &
%   \multicolumn{1}{c}{\multirow{2}{*}{\textbf{GCN}}} &
%   \multicolumn{1}{c}{\multirow{2}{*}{\textbf{GAT}}} &
%   \multicolumn{2}{c}{\textbf{Loss}} &
%   \multicolumn{5}{c}{\textbf{All}} &
%   \multicolumn{4}{c}{\textbf{Top-3}} \\ \cline{4-14} 
% \multicolumn{1}{c}{} &
%   \multicolumn{1}{c}{} &
%   \multicolumn{1}{c}{} &
%   \multicolumn{1}{c}{\textbf{\texttt{ASL}}} &
%   \multicolumn{1}{c}{\textbf{CSL}} &
%   \multicolumn{1}{c}{\textbf{mAP}} &
%   \multicolumn{1}{c}{\textbf{OP}} &
%   \multicolumn{1}{c}{\textbf{OF1}} &
%   \multicolumn{1}{c}{\textbf{CP}} &
%   \multicolumn{1}{c}{\textbf{CF1}} &
%   \multicolumn{1}{c}{\textbf{OP}} &
%   \multicolumn{1}{c}{\textbf{OF1}} &
%   \multicolumn{1}{c}{\textbf{CP}} &
%   \multicolumn{1}{c}{\textbf{CF1}} \\ \hline
% X & X & X & X & X & 96.80          & 89.20 & 92.07 & 89.23 & 92.07 & 49.60 & 65.54 & 47.84 & 63.87 \\ \hline
% X & \checkmark & X & \checkmark & X & 96.77          & 92.72 & 93.12 & 92.50 & 92.72 & 49.86 & 65.88 & 56.49 & 71.22 \\ \hline
% \checkmark & \checkmark & X & \checkmark & X & 96.75          & 92.76 & 93.11 & 92.72 & 92.72 & 49.86 & 65.88 & 56.54 & 71.24 \\ \hline
% X & X & \checkmark & \checkmark & X & 96.77          & 87.99 & 91.57 & 87.78 & 91.49 & 49.58 & 65.51 & 47.90 & 63.92 \\ \hline
% \checkmark & X & \checkmark & \checkmark & X & \textbf{96.85} & 86.68 & 91.04 & 86.75 & 91.05 & 49.63 & 65.57 & 49.16 & 65.03 \\ \hline
% X & \checkmark & X & X & \checkmark & 96.72          & 87.81 & 91.38 & 86.8  & 90.99 & 49.23 & 65.04 & 49.97 & 65.63 \\ \hline
% \checkmark & \checkmark & X & X & \checkmark & 96.78          & 86.73 & 91    & 85.7  & 90.56 & 49.4  & 65.27 & 48.48 & 64.49 \\ \hline
% X & X & \checkmark & X & \checkmark & 96.80          & 85.94 & 90.72 & 86.56 & 91.14 & 49.7  & 65.67 & 52.42 & 67.89 \\ \hline
% \checkmark & X & \checkmark & X & \checkmark & 96.79          & 81.43 & 88.44 & 81.05 & 88.23 & 49.55 & 65.47 & 48.1  & 64.08 \\ \hline
% \end{tabular}
% }
% \end{table}

% \begin{table}[]
% \centering
% \caption{Ablation study of fusion strategies and loss functions in \texttt{KGGAT} on VOC2017 multi-label classification. 
% }
% \label{tab:knowledge_sources}
%     \label{table:VOC2}
%     \resizebox{\columnwidth}{!}{%
% \begin{tabular}{cccccccccccccccccc}
% \hline
% \multicolumn{3}{c}{\textbf{Fusion Type}} &
%   \multicolumn{2}{c}{\textbf{Loss}} &
%   \multicolumn{7}{c}{\textbf{All}} &
%   \multicolumn{6}{c}{\textbf{Top-3}} \\ \hline
% \textbf{Add (+)} &
%   \textbf{Multiply (×)} &
%   \textbf{Concat + Linear} &
%   \textbf{\texttt{ASL}} &
%   \textbf{CSL} &
%   \textbf{mAP} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} &
%   \textbf{OP} &
%   \textbf{OR} &
%   \textbf{OF1} &
%   \textbf{CP} &
%   \textbf{CR} &
%   \textbf{CF1} \\ \hline
% \checkmark &
%   X &
%   X &
%   \checkmark &
%   X &
%   \textbf{96.85} &
%   \multicolumn{1}{l}{86.68} &
%   \multicolumn{1}{l}{95.86} &
%   \multicolumn{1}{l}{91.04} &
%   \multicolumn{1}{l}{86.75} &
%   \multicolumn{1}{l}{95.81} &
%   \multicolumn{1}{l}{91.05} &
%   \multicolumn{1}{l}{49.63} &
%   \multicolumn{1}{l}{96.61} &
%   65.57 &
%   49.16 &
%   96.04 &
%   65.03 \\ 
% X &
%   \checkmark &
%   X &
%   \checkmark &
%   X &
%   96.67 &
%   \multicolumn{1}{l}{93.74} &
%   \multicolumn{1}{l}{92.03} &
%   \multicolumn{1}{l}{92.88} &
%   \multicolumn{1}{l}{93.71} &
%   \multicolumn{1}{l}{91.43} &
%   \multicolumn{1}{l}{92.55} &
%   \multicolumn{1}{l}{49.77} &
%   \multicolumn{1}{l}{96.88} &
%   65.76 &
%   51.58 &
%   96.39 &
%   67.29 \\ 
% X &
%   X &
%   \checkmark &
%   \checkmark &
%   X &
%   96.81 &
%   \multicolumn{1}{l}{93.17} &
%   \multicolumn{1}{l}{92.79} &
%   \multicolumn{1}{l}{92.98} &
%   \multicolumn{1}{l}{92.76} &
%   \multicolumn{1}{l}{92.35} &
%   \multicolumn{1}{l}{92.55} &
%   \multicolumn{1}{l}{49.85} &
%   \multicolumn{1}{l}{97.04} &
%   65.87 &
%   57.50 &
%   96.40 &
%   72.03 \\ 
% \checkmark &
%   X &
%   X &
%   X &
%   \checkmark &
%   96.79 &
%   81.43 &
%   96.76 &
%   88.44 &
%   81.05 &
%   96.81 &
%   88.23 &
%   49.55 &
%   96.45 &
%   65.47 &
%   48.1 &
%   95.98 &
%   64.08 \\ 
% X &
%   \checkmark &
%   X &
%   X &
%   \checkmark &
%   96.48 &
%   86.75 &
%   95.7 &
%   91 &
%   86.52 &
%   95.53 &
%   90.8 &
%   49.67 &
%   96.69 &
%   65.63 &
%   48.89 &
%   96.07 &
%   64.8 \\ 
% X &
%   X &
%   \checkmark &
%   X &
%   \checkmark &
%   96.54 &
%   85.66 &
%   96.07 &
%   90.57 &
%   85.17 &
%   96.03 &
%   90.27 &
%   49.65 &
%   96.65 &
%   65.6 &
%   49.57 &
%   96.19 &
%   65.43 \\ \hline
% \end{tabular}
% }
% \end{table}

\begin{table*}[]
\centering
\caption{Effect of different knowledge sources in \texttt{GAT-based} \texttt{VOC 2007} classification. 
}
    \label{table:VOC3}
    \resizebox{\textwidth}{!}{%

\begin{tabular}{ccc|ccccccc|cccccc|c|c}
\hline
\multicolumn{3}{c|}{\textbf{Knowledge Source}} &
  \multicolumn{7}{c|}{\textbf{All}} &
  \multicolumn{6}{c|}{\textbf{Top-3}} &
  \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Total \\ Params\end{tabular}}} &
  \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Params\\  Size (MB)\end{tabular}}} \\ \cline{1-16}
\textbf{CN55} &
  \textbf{OA} &
  \textbf{OA-FU} &
  \textbf{mAP} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
  \textbf{OP} &
  \textbf{OR} &
  \textbf{OF1} &
  \textbf{CP} &
  \textbf{CR} &
  \textbf{CF1} &
   &
   \\ \hline \hline
\checkmark & X & X &$ 96.83$ & \textbf{88.12} & $95.31$ & \textbf{91.57} & \textbf{87.81} & $95.41$ & \textbf{91.46} & $49.61 $& $96.57 $& $65.55 $& $47.62 $& \textbf{96.10} & $63.68$ & $124,587,669 $& $296.31 $\\ 
X & \checkmark & X & $96.79 $& $86.97$ & $95.74 $& $91.14$ & $86.96$ & $95.83 $& $91.18$ & $49.6 $ & $96.55 $& $65.54$ & $47.5 $ & $96 $   & $63.55$ & $124,587,669$ &$ 296.31$ \\ 
X & X & \checkmark & \textbf{96.85} & $86.68$ &\textbf{ 95.86} & $91.04 $& $86.75$ & \textbf{95.81 }& $91.05$ & \textbf{49.63} & \textbf{96.61} & \textbf{65.57} & \textbf{49.16} & $96.04$ & \textbf{65.03} &$ 124,587,669$ &$ 296.31 $\\ \hline
\end{tabular}

}
\end{table*}


% \subsection{Discussion}
% \texttt{KGGAT} effectively bridges data-driven visual learning and knowledge-guided reasoning. 
% The image-conditioned graph attention adaptively reconfigures label dependencies according to image content, while the knowledge-consistency loss ensures that these dependencies remain semantically valid. 
% Together, they enable \texttt{KGGAT} to achieve high accuracy, strong generalization, and interpretable multi-label predictions.

