# Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method
## 1 Introduction  
Food image generation refers to generating visually realistic food images based on given ingredient descriptions [1]. Due to the advancement of AIGC (Artificial Intelligence Generated Content), notable progress has been made in food image generation [2; 3; 4]. However, generating high-quality food images remains a challenge. The main challenges of food image generation are as follows [5]: (1) semantic inconsistency, i.e., inconsistency between ingredient and food image data. This inconsistency poses a difficulty for models to precisely describe the correspondence between ingredients and images, thus affecting the precision of food image generation. (2) Insufficient visual realism: the generated images fail to express visual information of actual food images in detail and accurately. To ensure text-image semantic consistency, existing methods attempt to integrate text encoders and image encoders to establish cross-modal consistency, using LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Networks), respectively, as text encoders and as image encoders [6; 7]. However, the fixed input sequence length of LSTM might cause information loss, especially when dealing with diverse and high-dimensionaldata. Furthermore, LSTM heavily relies on extensive labeled data, thus potentially limiting performance in few-shot scenarios. In contrast, the Transformer relies on self-attention mechanisms to capture relationships between different positions in a sequence. Therefore, by analyzing information throughout the entire sequence, transformers can overcome the limitations of LSTM in text encoding, which contributes to establishing a more precise understanding of semantic context and relationships within the text [8]. However, this method lacks a joint learning process for image and text encoding. Consequently, it fails to fully exploit the latent cross-modal information, which might result in suboptimal performance. To achieve superior outcomes, it is imperative to explore joint learning methods for image-text encoding. Due to the insufficiency of visually realistic food images, most existing research relied on GANs for food image generation. For instance, SM-GAN [9] introduced user-drawn mask images to strengthen the reliability of generated images by reinforcing distinctions between plate masks and food masks. However, this approach resulted in lower-resolution images and is solely employed for retrieval models by regularization, consequently limiting the assessment of image quality. Differing from regularization retrieval models, CookGAN [10] adopted attention mechanisms and cycle-consistency constraints to enhance image quality and control appearance. PizzaGAN [11] employed CycleGAN to distinguish the presence or absence of various ingredients, in which different stages of pizza preparation can be simulated. Nevertheless, this approach only generated pizzas with predefined steps, which limited its capability to generate diverse food images. To address this limitation, ML-CookGAN [12] represented text information by various granularities in sentence or word levels, which can be transformed into different ingredients of the generated images with different sizes and shapes. To enhance the quality of generated images, ChefGAN [13] utilized joint embeddings of image and ingredient features to guide image generation. Based on GAN networks, the above methods are able to improve the clarity of the generated images to some extent. Although these GAN-based methods improve the clarity of generated images to some extent, there still exists imperfection in singular images with a lack of diverse visual features. Although researchers have conducted extensive exploration, food image generation still faces problems of low semantic consistency and insufficient visual realism. With the emergence of diffusion models [14], positioning food image generation methods based on diffusion models achieved promising results in efficiently generating more diverse food images.