Leveraging Human Salience to Improve Calorie Estimation

2023-06-15 · 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.

📄 PDF Abstract BibTeX arXiv:2306.09527

Code (1)

thearod5/calorie-predictor 공식 구현 tf

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Using Distance Estimation and Deep Learning to Simplify Calibration in Food Calorie Measurement

2015-02-11 · Pallavi Kuhad, Abdulsalam Yassine, Shervin Shirmohammadi

High calorie intake in the human body on the one hand, has proved harmful in numerous occasions leading to several diseases and on the other hand, a standard amount of calorie intake has been deemed essential by dieticia…

Cloud Computing

Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

2017-06-10 · Yanchao Liang, Jianhua Li

Obesity treatment requires obese patients to record all food intakes per day. Computer vision has been introduced to estimate calories from food images. In order to increase accuracy of detection and reduce the error of …

Deep Learning Object

RAG

Leveraging Human Salience to Improve Calorie Estimation

Code (1)

Methods 이 논문이 사용한 방법론

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