# Fast Fourier Asymmetric Context Aggregation Network:
## 3 Proposed Method
### The Context Aggregation and Decoding Stage
To provide a clearer explanation of the internal modeling details of ACAM, we conducted further analysis and interpretation of its feature modeling process using feature visualization techniques. For illustrative purposes, we take FCAN, which consists of four encoding blocks and four decoding blocks, as an example to present the visualization results. Figure 6 shows the visualization results of multi-scale features during the encoding phase. As shown in the figure, the shallow features in the encoding stage are more adept at capturing low-level information such as textures and edges, while deeper features are capable of extracting richer high-level semantic information. Next, we proceed based on the ACAM workflow described in this section. Figure 7 presents the visualization results of the context attention maps constructed by ACAM, where \(A_{4}\) and \(A_{3}\) represent the context attention maps constructed from \(F_{4}\) and \(F_{3}\), respectively. As shown in Figure 7, the context attention maps extracted from the deeper decoding features enhance global high-level information, while those extracted from the shallower features focus more on the expression of local structures and texture details. Thus, ACAM leverages cross-level and cross-stage heterogeneous structures for context aggregation, thereby enhancing the model's ability to represent diverse feature details. Figure 6: The visualization of multi-scale features from the encoding stage. Figure 7: The context attention visualization maps constructed by ACAM. Figure 8 presents the visualization results of the context-enhanced features \(F_{4}^{A}\) and \(F_{3}^{A}\) obtained using \(A_{4}\) and \(A_{3}\), respectively. As indicated, \(F_{4}^{A}\) achieves a more comprehensive aggregation of global information, while \(F_{3}^{A}\) provides a more detailed interpretation of local features. These results visually demonstrate the effectiveness of ACAM in heterogeneous feature aggregation. Finally, to facilitate a clear understanding of our overall process, we summarize the operation of our FCAN in the form of pseudocode. It is presented in Algorithm 1. ```
0: Sketch \(S\), Clothing Mask \(M_{c}\), and Texture Patch \(T\)
0: Synthesized Clothing Image \(C_{gen}\)
1: Obtain the clothing region positional information \(P\) via the sine-cosine encoding technique in [61]
2: Obtain the fused features as input \(f_{0}\): \(f_{0}\) = Concat(\(S\),\(M\),\(T\))
3: Obtain the multi-scale features \(f_{i},i\in[1,N]\):
4:for each \(i\in[1,N]\)do
5:if\(i=1\)then
6:\(f_{1}=Encoder_{1}(f_{0}\),\(P)\)
7:else
8:\(f_{i}=Encoder_{i}(f_{i-1})\)
9:endif
10:endfor
11: Content synthesis using R sequential FF-CDRB modules, and obtain the features \(F_{N}\):
12:for each \(n\in[1,R]\)do
13:if\(n=1\)then
14:\(f_{1}^{F}=FF-CDRB_{1}(f_{N})\)
15:else
16:\(f_{n}^{F}=FF-CDRB_{n}(f_{n-1}^{F})\)
17:endif
18:endfor
19: Decode the feature maps and use ACAM for context interaction between heterogeneous features:
20:\(F_{N}=f_{R}^{F}\)
21:for each \(i\in[N,1]\)do
22:if\(i=N\)then
23:\(F_{N-1}=Decoder_{1}(F_{N})\)
24:elseif\(i=1\)then
25:\(C_{gen}=Decoder_{N}(F_{1})\)
26:else
27:\(F_{N-i}=Decoder_{i}(F_{N-i+1})\oplus\text{ACAM}(F_{N-i+2}f_{N-i+1})\)
28:endif
29:endfor
30:return\(C_{\text{gen}}\)
```  
**Algorithm 1** Algorithm of the FCAN  
Figure 8: The visualization maps of context-enhanced features constructed by ACAM.