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\title{KGGAT: Knowledge-Guided Graph Attention Network for Multi-Label Image Classification}  % **** Enter the paper title here

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We thank all three reviewers for their constructive comments and for acknowledging the strengths of our submission. Below we address the main concerns and clarify points that may have been unclear in the paper.

\noindent\textcolor{blue}{\textbf{Reviewer 1Ju5 (R1)}}
\noindent\textbf{Response to R1-1 (significance / improvement).}
We respectfully disagree. Under our controlled reproduction (Sec.~4.3.1; TResNet-L+ASL, $448{\times}448$), the reproduced Q2L-TResL is 89.08 mAP (close to the reported 89.20; $\Delta{=}0.12$), while KGGAT achieves 89.21 mAP (+0.13) with improved CR/OR; Table~3 shows a consistent ablation trend (89.09$\rightarrow$89.21). The gain is clearer on VOC2007 (Table~2: 96.10$\rightarrow$96.85, +0.75). We will update Table~1 to include Q2L-TResL parameters ($\sim$120M) for transparent cost--benefit comparison.
\noindent\textbf{Response to R1-2 (novelty).}
We respectfully disagree. Prior semantic-prior MLIC works (e.g.,[1,4]) are also incremental, yet impactful because they improve strong baselines/SOTA. Similarly, our post-decoder knowledge-guided refinement for Q2L (with pruning/fusion) improves mAP and CR/OR and achieves best/near-best results (notably on VOC), which we will clarify as the key novelty/benefit.
\noindent\textbf{Response to R1-3 (image-conditioned attention vs.\ KCL).}
We disagree that KCL collapses $\alpha$ to the static prior $S$. Its effect is controlled by $\lambda_{\text{KCL}}$, which we set small so $\alpha$ remains image-conditioned; KCL only softly regularizes deviations to stabilize training, not to force fallback to $S$.
\noindent\textbf{Response to R1-4 (GAP vs.\ spatial information).}
We agree the Introduction wording can be misread. Our critique is about methods that rely \emph{only} on global pooling for recognition. In our model, spatial information is preserved by the Transformer’s token/region-level attention; $\bar{F}$ in Eq.~(2) is used only as a lightweight \emph{global context} signal to modulate label--label relations, not as the main feature for prediction. We will revise the Introduction to state this explicitly.
\noindent\textbf{Response to R1-5 (GAT rationale \& placement).}
We respectfully disagree. While Q2L’s decoder self-attention models label interactions, it is purely data-driven; our GAT refinement explicitly injects structured semantic priors and empirically improves over the Q2L baseline (Tables~2--3), including mAP and CR/OR. We will clarify this experimentally driven motivation in the revision.
\noindent\textbf{Response to R1-6 (comparison recency).}
We respectfully disagree. Table~1 already includes recent methods from 2023--2025 (e.g., C-TMS'23, HSQ'24, MambaML'25), ensuring the comparison reflects the latest progress.
\noindent\textbf{Response to R1-7 ($\eta$ / OA vs.\ ConceptNet).}
We use hyperparameters that are standard in this line of work and follow Q2L[14]’s protocol with fixed settings across COCO/VOC (defaults in L321). We partially address this by reporting ConceptNet-only vs.\ OpenAI-only vs.\ fused OA-FU in Tables~5--6. We highlight these results more clearly and temper claims about the OpenAI graph accordingly.
\noindent\textbf{Response to R1-8 (datasets).}
We agree more datasets could strengthen generality. We evaluate on MS-COCO and VOC2007, the standard MLIC benchmarks with established protocols, enabling direct comparison to Q2L/KG baselines. We will better justify this choice, limit claims to these benchmarks, and mention Visual Genome as future work (L323).

\noindent\textcolor{blue}{\textbf{Reviewer hwBu (R2)}}
\noindent\textbf{Response to R2-1 (Noisy/bias knowledge).} Knowledge is used only as label–label edge priors, and attention+residual can down-weight unreliable edges and fall back to visual logits. We observed no noticeable degradation from OpenAI-derived edges, and will study robustness under edge perturbation in future work.
\noindent\textbf{Response R2-2 (hyperparameters).}
We agree these hyperparameters are important. KGGAT is built on Q2L[14] and we follow the same hyperparameter settings/protocol as Q2L, using \emph{fixed} settings across COCO/VOC. We will further highlight the default values already stated in L321.
\noindent\textbf{Response R2-3 (compute/latency).}
We agree this is a valid point. Our focus was performance, consistent with Q2L[14], and we did not include FLOPs/latency analysis. We will acknowledge this limitation and add a brief FLOPs/inference-time report in the revision; a deeper efficiency–accuracy trade-off study is left to future work.
\noindent\textbf{Response R2-4 (MLLM comparison).}
We agree MLLMs are powerful, but in task-specific domains (e.g., medical/industrial) privacy/regulatory and deployment constraints often preclude off-the-shelf GPT/MLLM services; our setting requires full control to train and deploy on-premise models, which we will clarify.
\noindent\textbf{Response R2-5 (hard classes).}
We agree and appreciate the suggestion. In future work, we will refine the knowledge graph to better target hard/ambiguous classes (e.g., \emph{board}, \emph{chair}) with more class-specific discriminative relations.

\noindent\textcolor{blue}{\textbf{Reviewer VzPH (R3)}}
\noindent\textbf{Response to R3-1 (built on Q2L; similar performance).}
KGGAT is a post-decoder refinement of Q2L, so gains are incremental. Under our controlled reproduction (Sec.~4.3.1 and 4.3.2), it improves over the reproduced Q2L baseline and also increases CR/OR; the gain is clearer on VOC2007 (Table~2).
\noindent\textbf{Response to R3-2 (Table~3: components and  partial combination).}
The modules are complementary: \texttt{ASL} provides strong supervision, while \texttt{KGGAT} refines label dependencies with structured semantics. The full \texttt{KGGAT+ASL} achieves best/near-best on \texttt{MS-COCO} and \texttt{VOC 2007}; removing any part reduces performance/stability. We report \texttt{Top-3} scores for fair comparison.

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