Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design

doi:10.1145/3600095

	Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design
	Song, Jiajun; Li, Zhuo; Min, Weiqing; Jiang, Shuqiang
	2024
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	20 期号:1 页码:19
摘要	Food computing has increasingly received widespread attention in the multimedia field. As a basic task of food computing, food image retrieval has wide applications, that is, food image retrieval can help users to find the desired food from a large number of food images. Besides, the retrieved information can be applied to establish a richer database for the subsequent food content-related recommendation. Food image retrieval aims to achieve better performance on novel categories. Thus, it is worth studying to transfer the embedding ability from the training set to the unseen test set, that is, the generalization of the model. Food is influenced by various factors, such as culture and geography, leading to great differences between domains, such as Asian food and western food. Therefore, it is challenging to study the generalization of the model in food image retrieval. In this article, we improve the classical metric learning framework and propose a generalization-oriented sampling strategy, which boosts the generalization of the model by maximizing the intra-class distance from a proportion of positive pairs to avoid the excessive distance compression in the embedding space. Considering that the existing optimization process is in an opposite direction to our proposed sampling strategy, we further propose an adaptive gradient assignment policy named gradient-adaptive optimization, which can alleviate the intra-class distance compression during optimization by assigning different gradients to different samples. Extensive evaluation on three popular food image datasets demonstrates the effectiveness of the proposed method. We also experiment on three popular general datasets to prove that solving the problem from the generalization can also improve the performance of general image retrieval. Code is available at https://github.com/Jiajun-ISIA/Generalization- oriented- Sampling- and- Loss.
关键词	Food computing image retrieval deep learning
DOI	10.1145/3600095
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[61972378] ; National Nature Science Foundation of China[62125207] ; National Nature Science Foundation of China[U1936203] ; National Nature Science Foundation of China[U19B2040] ; CAAI-Huawei MindSpore Open Fund
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001080441800013
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21112
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Song, Jiajun
作者单位	Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Song, Jiajun,Li, Zhuo,Min, Weiqing,et al. Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(1):19.
APA	Song, Jiajun,Li, Zhuo,Min, Weiqing,&Jiang, Shuqiang.(2024).Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(1),19.
MLA	Song, Jiajun,et al."Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.1(2024):19.