# Shape-Preserving Generation of Food Images for Automatic Dietary Assessment

arXiv: https://arxiv.org/abs/2408.13358 · HTML: https://mlsanity.com/paper/2408.13358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [Rethinking the Truly Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2006.06500/) (2020)
- [ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network](https://mlsanity.com/paper/2008.05655/) (2020)
- [A Large-Scale Benchmark for Food Image Segmentation](https://mlsanity.com/paper/2105.05409/) (2021)
- [Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images](https://mlsanity.com/paper/1905.01273/) (2019)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image](https://mlsanity.com/paper/2308.01810/) (2023)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets](https://mlsanity.com/paper/2105.06219/) (2021)
- [MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs](https://mlsanity.com/paper/2012.02821/) (2020)
- [Diffusion Model with Clustering-based Conditioning for Food Image Generation](https://mlsanity.com/paper/2309.00199/) (2023)
- [FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation](https://mlsanity.com/paper/2312.03540/) (2023)
- [DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion](https://mlsanity.com/paper/2305.14849/) (2023)

## Built on by (most cited first, 1 of 1)

- [Mechanisms of Generative Image-to-Image Translation Networks](https://mlsanity.com/paper/2411.10368/) (2024)

## Abstract (arXiv)

Traditional dietary assessment methods heavily rely on self-reporting, which is time-consuming and prone to bias. Recent advancements in Artificial Intelligence (AI) have revealed new possibilities for dietary assessment, particularly through analysis of food images. Recognizing foods and estimating food volumes from images are known as the key procedures for automatic dietary assessment. However, both procedures required large amounts of training images labeled with food names and volumes, which are currently unavailable. Alternatively, recent studies have indicated that training images can be artificially generated using Generative Adversarial Networks (GANs). Nonetheless, convenient generation of large amounts of food images with known volumes remain a challenge with the existing techniques. In this work, we present a simple GAN-based neural network architecture for conditional food image generation. The shapes of the food and container in the generated images closely resemble those in the reference input image. Our experiments demonstrate the realism of the generated images and shape-preserving capabilities of the proposed framework.