Cross-modal food retrieval: Learning a joint embedding of food images and recipes with semantic consistency and attention mechanism

Cross-modal food retrieval: Learning a joint embedding of food images and recipes with semantic consistency and attention mechanism

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 cook...

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Bibliographic Details
Main Authors: WANG, Hao, SAHOO, Doyen, LIU, Chenghao, SHU, Ke, ACHANANUPARP, Palakorn, LIM, Ee-peng, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Correlation
Cross-Modal Retrieval
Data models
Deep Learning
Semantics
Sugar
Task analysis
Training
Visionand-Language
Visualization
Artificial Intelligence and Robotics
Databases and Information Systems
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/6268
https://ink.library.smu.edu.sg/context/sis_research/article/7271/viewcontent/cross_modal_food_retrieval.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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