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The following paper investigates the effectiveness of incorporating humansalience into the task of calorie prediction from images of food. We observe a32.2% relative improvement when incorporating saliency maps on the images offood highlighting the most calorie regions. We also attempt to further improvethe accuracy by starting the best models using pre-trained weights on similartasks of mass estimation and food classification. However, we observe noimprovement. Surprisingly, we also find that our best model was not able tosurpass the original performance published alongside the test dataset,Nutrition5k. We use ResNet50 and Xception as the base models for ourexperiment.

Leveraging Human Salience to Improve Calorie Estimation