## 4 Results  
### Overall Classification Performance  
The proposed CLIP-BCA-Gated model was evaluated against 24 baseline models on the CrisisMMD dataset, demonstrating significant performance superiority across all metrics.  
As shown in Table 3, the model achieved an accuracy of 91.77 \(\pm\) 0.11%, outperforming the baseline CLIP model (90.22 \(\pm\) 0.12%) by 1.55 percentage points. For critical disaster categories, the model exhibited exceptional recall:  
\begin{tabular}{l c c c c} \(\bullet\) & Infrastructure\_and\_utility\_damage: 93.42\%; \\ \(\bullet\) & Rescue\_volunteering\_or\_donation\_effort: 92.15\%; \\ \(\bullet\) & Affected\_individuals: 89.73\%. \\ \end{tabular}  
The F1 score across all classes reached 91.74 \(\pm\) 0.11%, indicating balanced precision and recall. This performance surpasses both unimodal baselines (e.g., CLIP-Text:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline VGG-16 + CNN [6] & 78.4 \(\pm\) 0.5 & 78.5 \(\pm\) 0.4 & 78.0 \(\pm\) 0.6 & 78.3 \(\pm\) 0.5 \\ VGG-16 + CNN (Image only) & 76.8 \(\pm\) 0.3 & 76.4 \(\pm\) 0.3 & 76.8 \(\pm\) 0.4 & 76.3 \(\pm\) 0.3 \\ VGG-16 + CNN (Text only) & 70.4 \(\pm\) 0.6 & 70.0 \(\pm\) 0.5 & 70.0 \(\pm\) 0.5 & 67.7 \(\pm\) 0.7 \\ DenseNet + BERT [37] & 82.72 \(\pm\) 0.3 & 82.50 \(\pm\) 0.3 & 82.72 \(\pm\) 0.2 & 82.46 \(\pm\) 0.3 \\ FBP with fusion [38] & – & 88.5 \(\pm\) 0.2 & 88.1 \(\pm\) 0.2 & 88.1 \(\pm\) 0.2 \\ CBAN-Dot [39] & 88.38 \(\pm\) 0.2 & 87.95 \(\pm\) 0.2 & 87.80 \(\pm\) 0.2 & 88.40 \(\pm\) 0.2 \\ DMCC [40] & 88.00 \(\pm\) 0.2 & 87.95 \(\pm\) 0.2 & 87.80 \(\pm\) 0.2 & 87.72 \(\pm\) 0.2 \\ CLIP [1] & 90.22 \(\pm\) 0.12 & 90.23 \(\pm\) 0.11 & 90.22 \(\pm\) 0.12 & 90.04 \(\pm\) 0.12 \\ CLIP (Image only) [1] & 87.43 \(\pm\) 0.21 & 87.48 \(\pm\) 0.22 & 87.43 \(\pm\) 0.21 & 87.14 \(\pm\) 0.26 \\ CLIP (Text only) [1] & 81.26 \(\pm\) 0.32 & 81.47 \(\pm\) 0.31 & 81.26 \(\pm\) 0.32 & 80.70 \(\pm\) 0.41 \\ CLIP Surgery [1] & 90.21 \(\pm\) 0.11 & 90.23 \(\pm\) 0.12 & 90.21 \(\pm\) 0.11 & 90.02 \(\pm\) 0.14 \\ CLIP Surgery (Image only) [1] & 87.49 \(\pm\) 0.27 & 87.51 \(\pm\) 0.22 & 87.49 \(\pm\) 0.27 & 87.26 \(\pm\) 0.19 \\ CLIP Surgery (Text only) [1] & 81.14 \(\pm\) 0.33 & 81.18 \(\pm\) 0.31 & 81.14 \(\pm\) 0.33 & 80.65 \(\pm\) 0.36 \\ ALIGN [1] & 89.44 \(\pm\) 0.18 & 89.40 \(\pm\) 0.18 & 89.44 \(\pm\) 0.18 & 89.31 \(\pm\) 0.19 \\ ALIGN (Image only) [1] & 86.49 \(\pm\) 0.18 & 86.58 \(\pm\) 0.17 & 86.49 \(\pm\) 0.18 & 86.20 \(\pm\) 0.18 \\ ALIGN(Text only) [1] & 80.63 \(\pm\) 0.21 & 80.63 \(\pm\) 0.22 & 80.63 \(\pm\) 0.21 & 80.40 \(\pm\) 0.25 \\ ALIGN-Concat-Aug & 89.91 \(\pm\) 0.1 & 89.92 \(\pm\) 0.1 & 89.91 \(\pm\) 0.1 & 89.85 \(\pm\) 0.1 \\ ALIGN-Img-Aug & 86.08 \(\pm\) 0.2 & 86.62 \(\pm\) 0.2 & 86.08 \(\pm\) 0.2 & 86.14 \(\pm\) 0.2 \\ ALIGN-Txt-Aug & 82.72 \(\pm\) 0.2 & 82.68 \(\pm\) 0.2 & 82.72 \(\pm\) 0.2 & 82.70 \(\pm\) 0.2 \\ FLAVA-Concat-Aug & 89.60 \(\pm\) 0.1 & 89.59 \(\pm\) 0.1 & 89.60 \(\pm\) 0.1 & 89.56 \(\pm\) 0.1 \\ FLAVA-Img-Aug & 82.80 \(\pm\) 0.3 & 83.14 \(\pm\) 0.3 & 82.80 \(\pm\) 0.3 & 82.81 \(\pm\) 0.3 \\ FLAVA-Txt-Aug & 79.20 \(\pm\) 0.4 & 79.04 \(\pm\) 0.4 & 79.20 \(\pm\) 0.4 & 79.02 \(\pm\) 0.4 \\ CLIP-Img-Aug & 87.18 \(\pm\) 0.2 & 87.16 \(\pm\) 0.2 & 87.18 \(\pm\) 0.2 & 87.14 \(\pm\) 0.2 \\ CLIP-Txt-Aug & 83.03 \(\pm\) 0.2 & 82.87 \(\pm\) 0.2 & 83.03 \(\pm\) 0.2 & 82.85 \(\pm\) 0.2 \\ CLIP-BCA-Gated & 91.77 \(\pm\) 0.11 & 91.81 \(\pm\) 0.10 & 91.77 \(\pm\) 0.11 & 91.74 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multimodal fusion model performance on CrisisMD.  
81.26 \(\pm\) 0.32%) and advanced multimodal models (e.g., ALIGN: 89.44 \(\pm\) 0.18%), validating the effectiveness of bidirectional cross-attention and adaptive gating.