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Qualifying Exam

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

 

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Tuesday, October 19, 2021, 02:00pm - 03:30pm

 

Speaker: Jiatong Li

Location : Remote via Zoom

Committee

Prof. Vladimir Pavlovic (Chair)

Prof. Dimitris Metaxas

Prof. Sungjin Ahn

Prof. David Pennock

Event Type: Qualifying Exam

Abstract: Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize the ingredients, a detailed analysis of their amounts in the meal, which is paramount for estimating the correct nutrition, is usually ignored. Here, we study the novel and challenging problem of predicting the relative amount of each ingredient from a food image. We propose PITA, the Picture-to-Amount deep learning architecture to solve the problem. More specifically, we predict the ingredient amounts using a domain-driven Wasserstein loss from image-to-recipe cross-modal embeddings learned to align the two views of food data. Experiments on a dataset of recipes collected from the Internet show the model generates promising results and improves the baselines on this challenging task.

 

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