Leveraging Human Salience to Improve Calorie Estimation
Katherine R. Dearstyne Alberto D. Rodriguez
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.