Paper Info
Title
Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images
Abstract
Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to learn a common embedding feature space between the two modalities, in which our approach consists of several novel ideas: (i) learning by using a new triplet loss scheme together with an effective sampling strategy, (ii) imposing modality alignment using an adversarial learning strategy, and (iii) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality. ACME achieves the state-of-the-art performance on the benchmark Recipe1M dataset, validating the efficacy of the proposed technique.
Year
DOI
Venue
2019
10.1109/CVPR.2019.01184
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Modalities,Food consumption,Open research,Feature vector,Embedding,Computer science,Artificial intelligence,Sampling (statistics),Machine learning,Modal,Adversarial system
Journal
abs/1905.01273
ISSN
Citations 
PageRank 
1063-6919
5
0.41
References 
Authors
0
5
Name
Order
Citations
PageRank
Hao Wang1163.28
Doyen Sahoo2839.94
Chenghao Liu333432.66
Ee-Peng Lim45889754.17
Steven C. H. Hoi53830174.61