Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2021

Abstract

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these two problems, we propose Semantic-Consistent and Attentionbased Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. Besides, we exploit a self-attention mechanism to improve the embedding of recipes.We evaluate the performance of the proposed method on the large-scale Recipe1M dataset, and show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.

Keywords

Correlation, Cross-Modal Retrieval, Data models, Deep Learning, Semantics, Sugar, Task analysis, Training, Visionand-Language, Visualization

Discipline

Graphics and Human Computer Interfaces | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Multimedia

ISSN

1520-9210

Identifier

10.1109/TMM.2021.3083109

Publisher

IEEE

Embargo Period

11-9-2021

Additional URL

10.1109/TMM.2021.3083109

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