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.

翻译：本文研究了将人类显著性信息融入食物图像热量预测任务中的有效性。我们在食物图像上使用显著性图突出显示热量最高的区域，观察到相对改进达到32.2%。我们还尝试通过使用在质量估计和食物分类等类似任务上预训练的权重来初始化最优模型，从而进一步提高准确性。然而，我们并未观察到改进。令人惊讶的是，我们还发现最优模型未能超越测试数据集Nutrition5k原始发布时的性能。我们在实验中使用ResNet50和Xception作为基础模型。