## 2 Methodology
### Design Concept Extraction Module  
For text-to-image tasks, an effective text encoder is crucial for accurately extracting semantic information from textual descriptions. Contrastive learning is particularly effective for obtaining a pre-trained text encoder as it strengthens the encoder's ability to extract salient features from text prompts by maximizing the alignment of semantically relevant pairs while minimizing the similarity of unrelated ones. According to the principles of Contrastive Language-Image Pre-training (CLIP) [28], the DCE module uses a contrastive text-image matching model (CTIMM) to obtain an effective text encoder, as shown in Figure 2. This model comprises text and image encoder networks dedicated to extracting text and image features, respectively, and maps them to a shared semantic space. By utilizing contrastive learning, the model establishes meaningful associations between conceptual sketches and their corresponding textual descriptions. The objective of CTIMM optimization is to maximize the similarity between the features of each sketch and its corresponding text features within the semantic space. Figure 1: Overall architecture of the proposed method. DCE and AAG refer to the design concept extraction and architectural appearance generation modules, respectively. The AAG module contains a deep sparse and text fusion generative adversarial network (DSTF-GAN) and a pre-trained rendering (PR) module. Figure 2: Framework of the contrastive text–image matching model. The text encoder incorporates a bidirectional Long Short-Term Memory (BiLSTM) architecture [29] to extract multi-level semantic information and a multi-head attention layer [30] that re-extracts low-level semantic information to augment text semantics and map to the shared semantic space. Within the BiLSTM, each word corresponds to two hidden states, which can be concatenated to obtain the low-level semantic information represented by \(\mathbf{h_{i}}\in\mathbb{R}^{512}(i=1,\ldots,n)\). The last hidden state \(\mathbf{h_{n}}\in\mathbb{R}^{512}\) is selected as the text semantic vector. Furthermore, the multi-head attention layer is designed to re-extract low-level semantics \(\mathbf{h_{i}}\in\mathbb{R}^{512}(i=1,\ldots,n-1)\), yielding supplementary semantic information \(\mathbf{h^{\prime}}\in\mathbb{R}^{512}\). Finally, by combining \(\mathbf{h^{\prime}}\) with \(\mathbf{h_{n}}\), the final global text semantic feature \(\mathbf{T}\in\mathbb{R}^{512}\) is obtained in the shared semantic space. The text encoder can be formulated as  
\[(\mathbf{w}_{1},\ldots,\mathbf{w_{n}})=\mathbf{M}_{300\times\mathbf{n_{w}}}(\mathbf{word_{1}}, \ldots,\mathbf{word_{n}}) \tag{1}\]  
\[\mathbf{h_{1}},\ldots,\mathbf{h_{n}}=F_{BiLSTM}(\mathbf{w}_{1},\ldots,\mathbf{w_{n}}) \tag{2}\]  
\[\mathbf{h^{\prime}}=F_{Attn}(\mathbf{h}_{1},\ldots,\mathbf{h_{n-1}}) \tag{3}\]  
\[\mathbf{T}=\mathbf{h_{n}}+\mathbf{h^{\prime}} \tag{4}\]  
where \(\mathbf{word_{i}}(i=1,\ldots,n)\) denotes the one-hot encoding of the \(i\)-th word in the description based on the word pool consisting of words in the dataset, \(\mathbf{M_{300\times n_{w}}}\in\mathbb{R}^{300\times n_{w}}\) is the semantic embedding matrix for obtaining the semantic embedding vector of each word in the description, \(n\) is the number of words in the description, \(n_{w}\) denotes the total number of words in the dataset, and \(F_{BiLSTM}\) and \(F_{Attn}\) denote the BiLSTM network and multi-head attention layer, respectively.