# Text-Guided Customizable Image Synthesis and Manipulation
## 4 Experimental Results
### Quantitative Results  
We employed the Inception Score (IS) [9] and FID [34] as quantitative evaluation methods. The IS can be used to evaluate the diversity and authenticity of synthetic images, and FID can be used to evaluate the authenticity of synthetic images. Table 2 shows the quantitative comparison results.  
**Comparison with T2I Methods.** The last two rows (d,e) in Table 2 are our proposed method, including image custom synthesis and image manipulation. Compared with the T2I methods (Table 2a) that use only text for image synthesis, our method surpassed the performance of most methods. The FID performance was not weaker than that of the current excellent T2I methods. Moreover, since our method allowed manual input of the text and contour information as well as using new text to modify the composite image, it outperformed the T2I methods in terms of applicability.  
**Comparison with Custom Methods.** Table 2b shows the results of current custom image synthesis methods. They use text combined with additional information (bounding box, key points, and contour) to synthesize images. In contrast, our customized method  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**CUB**} & \multicolumn{2}{c}{**MS COCO**} \\ \hline
**Method** & **Model** & **IS \(\uparrow\)** & **FID \(\downarrow\)** & **IS \(\uparrow\)** & **FID \(\downarrow\)** \\ \hline \multirow{4}{*}{(a) T2I} & GAN-CLS [10] & 2.88 & 68.79 & 7.88 & 60.62 \\  & StackGAN [11] & 3.7 & 35.11 & 8.45 & 74.05 \\  & StackGAN++ [35] & 4.04 & 18.02 & 8.3 & 81.59 \\  & AttnGAN [5] & 4.36 & 23.98 & 25.89 & 35.49 \\ \hline \multirow{4}{*}{(b) Custom} & GAWWN [15] & 3.62 & 53.51 & n/a & n/a \\  & Customizable- & 3.12 & 65.36 & n/a & n/a \\  & GAN [16] & 2.24 & n/a & n/a & n/a \\ \hline \multirow{4}{*}{(c) Manipulation} & TSGAN [19] & 2.24 & n/a & n/a & n/a \\  & TAGAN [20] & 3.32 & n/a & n/a & n/a \\  & ManiGAN [21] & 3.84 & 17.89 & 6.99 & n/a \\ \hline \multirow{4}{*}{(d) Manipulation} & Custom stage & 3.98 & 23.19 & 15.03 & 46.02 \\  & Manipulation & 4.03 & 18.63 & 16.53 & 36.74 \\ \hline \multirow{4}{*}{(e) Baseline + DM} & Custom stage & 4.02 & 18.72 & 15.27 & 45.04 \\  & Manipulation stage & 4.07 & 16.24 & 17.34 & 30.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantitative comparison results among the T2I methods (a), custom methods (b), manipulation methods (c), and our method (d,e) are shown below.  
Figure 7: The qualitative comparison results between CustomizableGAN [16] and our customized method are shown above. Both the text and contour information in the figure were input manually.  
outperformed them in terms of the IS and FID, demonstrating that our method had the best performance in customizable synthesis.  
**Comparison with Manipulation Methods.** Table 2c shows the results of current image manipulation methods, in which text was used to modify the original image content. Our manipulation method showed superiority over them, too.  
**Ablation Study.** Table 2d,e shows the ablation comparison experiment. We conducted ablation research on word-level attention, where baseline refers to the attention mechanism in [29] and Baseline + DM means that the dynamic memory method [30] was introduced on top of the baseline. The comparison demonstrates that the introduction of the dynamic memory method can improve the quality of synthesis results.