# Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method
## 6 Conclusions  
In this paper, we propose a novel network for food image generation from food ingredients. It first extracts features from the input ingredient and image learned from the CLIP module, then the ingredient-image pairs from the CLIP module are stored in the Memory module, while a pre-trained diffusion model is employed to generate the initial image due to the enhancement of the visual realism of the generated images. Finally, the attention fusion module refines the details of the generated images. Experiment results on the Mini-food dataset demonstrate the superiority of MLA-Diff compared to state-of-the-art methods, with an average decrease of 4.758 in FID, 0.733 in LPIPS, and an average increase of 2.450 in IS. The reason for this is that our MLA-Diff can capture the ingredient information more efficiently and enhance the generated images with more realistic details that are consistent with the input ingredients. Nevertheless, the methodology did not incorporate the processing of multimodal datasets, thereby neglecting the comprehensive analysis of multimodal information, which is essential for a more profound understanding of food ingredient data. Furthermore, the method failed to account for the nuanced dietary requirements that vary according to distinct culinary styles, national origins, and geographical regions. The future work primarily concentrates on two aspects. Firstly, we intend to incorporate additional information besides ingredient data, such as recipes, video frames, etc., during network training. Analyzing multi-category information provides a better comprehension of food ingredient data in the food image generation process. Secondly, a more powerful pre-trained model will be introduced to enable it to be capable of generating food images with different styles, angles, or specific requirements for various application fields. [PERSON], [PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] conducted the experiments, interpreted the data, and drafted the manuscript. [PERSON] provided professional suggestions. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (62462001), the Natural Science Foundation of Ningxia (2023 AAC03264), Basic Scientific Research in Central Universities of North Minzu University (2023 ZRLG02), and the Computer Vision and Virtual Reality innovation team of North Minzu University. Not applicable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **CmA (Q, K, V)** & **MCA** & **CoA** & **Diffusion** & **FID** & **LPIPS** & **IS** \\ \hline
1 & \((T_{i},T_{i},F_{en})\) & \(\surd\) & \(\surd\) & & 9.259 & 0.735 & 5.210 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 6.240 & 0.732 & 5.882 \\ \hline
2 & \((T_{i},F_{en},F_{en})\) & \(\surd\) & \(\surd\) & & 9.321 & 0.735 & 5.179 \\  & \(\surd\) & \(\surd\) & \(\surd\) & & 6.336 & 0.731 & 5.858 \\ \hline
3 & \((Tm_{q},Tm_{q},F_{en})\) & \(\surd\) & \(\surd\) & & 9.538 & 0.733 & 5.209 \\  & \(\surd\) & \(\surd\) & \(\surd\) & & 6.478 & 0.729 & 5.890 \\ \hline
4 & \((Tm_{q_{r}},F_{en},F_{en})\) & \(\surd\) & \(\surd\) & & 9.289 & 0.735 & 5.028 \\  & \(\surd\) & \(\surd\) & \(\surd\) & & 6.480 & 0.729 & 5.879 \\ \hline
5 & \((T_{i},T_{i},F_{en})\), \(\big{(}Tm_{q_{r}},Tm_{q_{r}},F_{en})\) & \(\surd\) & \(\surd\) & & 7.151 & 0.736 & 5.223 \\  & \(\surd\) & \(\surd\) & \(\surd\) & & 4.662 & 0.742 & 5.881 \\ \hline
6 & \((T_{i},F_{en},F_{en})\), \((T_{i},F_{en},F_{en})\) & \(\surd\) & \(\surd\) & & 6.709 & 0.734 & 5.084 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 4.665 & 0.741 & 5.946 \\ \hline
7 & \((T_{i},T_{i},F_{en})\), \((T_{i},F_{en},F_{en})\), \((Tm_{q_{r}},Tm_{q_{r}},F_{en})\), \((Tm_{q_{r}},F_{en},F_{en})\) & \(\surd\) & & 6.344 & 0.743 & 5.496 \\  & \(\surd\) & \(\surd\) & \(\surd\) & **4.285** & **0.733** & **6.366** \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table} 
Table 4: Effect of the pre-trained diffusion model.