## 4 Discussion
### Comparison of Different Text Fusion Methods
For the new dataset consisting of the generated high-rise conceptual sketches and corresponding texts, the CM precision was calculated as follows:  
\[\text{CM}-\text{precision}=\frac{\sum_{i=1}^{n_{g}}\sum_{j=1}^{b_{g}}g\Big{(} \textit{logits}_{\textit{bg}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{ \textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{ \textit{g}}}}}^{i}},logits_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{ \textit{g}}_{\textit{g}_{\textit{g}_{\textit{g}}}}^{i}},j}}},j}}}}^{i}}, \Big{)}}}{n_{g}\times b_{g}} \tag{28}\]  
where \(\textit{logits}_{\textit{g}}^{i}\) is the cosine similarity matrix of the \(i\)-th batch on the newly generated dataset; \(\textit{logits}_{\textit{bg}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{ \textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g}_{ \textit{g}}}}^{i}}},j}}}}}}^{i}}\) is the \(j\)-th row of \(\textit{logits}_{\textit{g}}^{i}\), representing the similarity of the \(j\)-th text to every generated sketch in the batch; \(\textit{logits}_{\textit{bg}_{\textit{g}_{\textit{g}_{\textit{g}_{\textit{g} \textit{g}_{\textit{g}_{\textit{g}}}^{i}}}}}}^{i}\) is the \(j\)-th column of \(\textit{logits}_{\textit{g}}^{i}\), representing the similarity of the \(j\)-th generated sketch to every text in the batch; \(n_{g}\) and \(b_{g}\) are the total number of batches and the batch size, respectively; and \(g\) is expressed in Equation (26). A higher CM precision indicates a better match between the generated sketch and text semantics. To calculate the FID, we generated five distinct sketches for each text description in the validation set by altering the noise vector \(\textit{z}\), and computed the FID by comparing the generated sketches with the corresponding real sketches. To calculate the CM precision, we grouped the generated sketch-text pairs into batches of eight, calculated the CM precision for each batch, and then averaged the results across all batches to obtain the final CM precision. Table 1 presents the experimental results, revealing that the proposed DSTF-GAN outperformed the other methods in terms of the FID and CM precision metrics. These quantitative comparisons provide evidence for the efficacy of the fusion approach employed in the DSTF-GAN for text-to-sketch synthesis. The results underscore that incorporating the sparsity of sketches into the generative process significantly enhances quality and semantic fusion. The conceptual sketches generated from various fusion methods using the same descriptions are shown in Figure 15. The results show that the Diver fusion method only captured certain architectural features and failed to generate complete high-rise sketches, whereas the DF fusion method could only generate realistic but not well-aligned high-rise building sketches. For instance, as depicted in Figure 15b, both DF and the DSTF-GAN produced high-rise building sketches with a broader base and a pole. However, only the DSTF-GAN incorporated a dome structure at the top, demonstrating the superior accuracy of the proposed DSTF fusion method in the synthesis of sketches. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Text Fusion Method** & **FID \(\downarrow\)** & **CM Precision (\%) \(\uparrow\)** \\ \hline Diver & 116.99 & 6.81 \\ DF & 25.88 & 27.64 \\
**DSTF (the proposed method)** & **25.73** & **36.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FID and CM precision results of three fusion methods.