Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input. Deep learning-based techniques are commonly used to perform image analysis such as food classification, segmentation, and portion size estimation, which rely on large amounts of food images with annotations for training. However, such data dependency poses significant barriers to real-world applications, because acquiring a substantial, diverse, and balanced set of food images can be challenging. One potential solution is to use synthetic food images for data augmentation. Although existing work has explored the use of generative adversarial networks (GAN) based structures for generation, the quality of synthetic food images still remains subpar. In addition, while diffusion-based generative models have shown promising results for general image generation tasks, the generation of food images can be challenging due to the substantial intra-class variance. In this paper, we investigate the generation of synthetic food images based on the conditional diffusion model and propose an effective clustering-based training framework, named ClusDiff, for generating high-quality and representative food images. The proposed method is evaluated on the Food-101 dataset and shows improved performance when compared with existing image generation works. We also demonstrate that the synthetic food images generated by ClusDiff can help address the severe class imbalance issue in long-tailed food classification using the VFN-LT dataset.

翻译：基于图像的膳食评估利用用餐场景图像作为输入，是一种高效且准确的营养摄入记录与分析方案。基于深度学习的技术常被用于食品分类、分割及份量估计等图像分析任务，这些技术依赖大量带标注的食品图像进行训练。然而，这种数据依赖性为实际应用带来了显著障碍，因为获取大规模、多样且均衡的食品图像数据集颇具挑战性。一种潜在解决方案是采用合成食品图像进行数据增强。尽管现有研究已探索使用基于生成对抗网络（GAN）的结构进行图像生成，但合成食品图像的质量仍有待提升。此外，虽然基于扩散的生成模型在通用图像生成任务中展现出良好效果，但由于食品图像具有显著的类内方差，其生成面临特殊挑战。本文研究基于条件扩散模型的合成食品图像生成方法，提出一种名为ClusDiff的高效聚类训练框架，用于生成高质量且具有代表性的食品图像。在Food-101数据集上的评估表明，该方法相比现有图像生成工作具有更优性能。我们还证明，利用VFN-LT数据集，ClusDiff生成的合成食品图像有助于缓解长尾食品分类中严重的类别不平衡问题。