## 3 Experiments and Results
### Experimental Results  
Training the text encoder. To evaluate and acquire an efficient pre-trained text encoder, the metric should signify the highest cosine similarity values between the corresponding image-text and text-image pairs within the validation set. Accuracy was computed using the following formula:  
\[Acc=\frac{\sum_{i=1}^{n}\sum_{j=1}^{b}g\Big{(}\mathbf{logits}_{i,j}^{i},\mathbf{logits}_{i,j}^{i},j\Big{)}}{n\times b} \tag{25}\]  
\[g(\mathbf{x},\mathbf{y},j)=\left\{ \begin{array}{ll}1\,,&if\ argmax(\mathbf{x})=j\text{ and }argmax(\mathbf{y})=j\\ 0\,,&otherwise\end{array} \right. \tag{26}\]  
where \(\mathbf{logits}^{i}\) is the cosine similarity matrix of the \(i\)-th batch; \(\mathbf{logits}_{i,j}^{i}\) is the \(j\)-th row of \(\mathbf{logits}^{i}\), representing the similarity of the \(j\)-th text to every sketch in the batch; \(\mathbf{logits}_{i,j}^{i}\) is the \(j\)-th column of \(\mathbf{logits}^{i}\), representing the similarity of the \(j\)-th sketch to every text in the batch; \(n\) and \(b\) are the total number of batches and the batch size, respectively; and \(\mathbf{x},\mathbf{y}\) denote two vectors. A higher \(Acc\) indicates a stronger alignment between the sketch and its corresponding text description, reflecting more accurate semantic extraction. The dataset was randomly split into training and validation sets on five occasions, and the model's matching accuracy was evaluated for each partition. During training, we randomly selected a sketch-text pair from a set of 32 generated sketches and the corresponding 10 distinct textual descriptions for each epoch. Figure 8 shows the \(Acc\) comparisons of the CTIMM under various dataset divisions, incorporating a semantic re-extraction layer and different data augmentation techniques, including random image scaling and cropping (DA) during the training process, the pre-trained latent stable diffusion model for image augmentation (SD), and ChatGPT for text augmentation (GPT). The experimental results onthe validation set demonstrate that these techniques consistently improved the matching accuracy between sketches and text descriptions, regardless of the dataset partitioning. Figure 9 shows the matching outcomes of one batch in Division 1 of the dataset. The results demonstrate that the pre-trained text encoder effectively extracted design concepts from textual descriptions and provided the necessary conditions for the subsequent generation of high-rise building images. Figure 8: Matching the accuracy of experiments in different divisions of the dataset with different data augmentation techniques. “Original” denotes training the CTIMM without any data augmentations. “DA” denotes training the model with scaling and cropping operations for data augmentation. “SD” and “GPT” denote that the pre-trained latent stable diffusion model and ChatGPT are adopted for data augmentation, respectively. Figure 9: _Cont_. Figure 9: One batch of matching results of “DA and SD and GPT” on the validation set of Division 1. Generation of high-rise building images. During the training of the DSTF-GAN, we selected the data partition that achieved the highest \(Acc\) along with the corresponding text encoder, as this partition is believed to better capture the features of high-rise buildings described in the textual descriptions. The performance of the trained DSTF-GAN was then qualitatively assessed on the validation set to evaluate its generative capabilities. Figure 10 shows the ability of the DSTF-GAN within the AAG module to produce a varied array of semantically consistent conceptual sketches of high-rise buildings using the same design concepts. However, the implicit biases present in the dataset can affect the generated sketches. For example, since the tower-type buildings in the dataset are primarily cultural landmarks, inputting the term \"tower\" tends to generate sketches that closely resemble these cultural structures.