학술논문
A Cross-Modal Variational Framework For Food Image Analysis
Document Type
Conference
Source
2020 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2020 IEEE International Conference on. :3244-3248 Oct, 2020
Subject
Language
ISSN
2381-8549
Abstract
Food analysis resides at the core of modern nutrition recommender systems, providing the foundation for a high-level understanding of users’ eating habits. This paper focuses on the sub-task of ingredient recognition from food images using a variational framework. The framework consists of two variational encoder-decoder branches, aimed at processing information from different modalities (images and text), as well as a variational mapper branch, which accomplishes the task of aligning the distributions of the individual branches. Experimental results on the Yummly-28K data-set showcase that the proposed framework performs better than similar variational frameworks, while it surpasses current state-of-the-art approaches on the large-scale Recipe1M data-set.