# Leveraging Human Salience to Improve Calorie Estimation

arXiv: https://arxiv.org/abs/2306.09527 · HTML: https://mlsanity.com/paper/2306.09527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning](https://mlsanity.com/paper/2112.00686/) (2021)
- [Computer vision-based food calorie estimation: dataset, method, and experiment](https://mlsanity.com/paper/1705.07632/) (2017)

## Abstract (arXiv)

The following paper investigates the effectiveness of incorporating human salience into the task of calorie prediction from images of food. We observe a 32.2% relative improvement when incorporating saliency maps on the images of food highlighting the most calorie regions. We also attempt to further improve the accuracy by starting the best models using pre-trained weights on similar tasks of mass estimation and food classification. However, we observe no improvement. Surprisingly, we also find that our best model was not able to surpass the original performance published alongside the test dataset, Nutrition5k. We use ResNet50 and Xception as the base models for our experiment.