Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss
文献类型：期刊论文
| 作者 | Luo, Mengjiang1,3; Min, Weiqing1,2,4; Wang, Zhiling3; Song, Jiajun1,2,3; Jiang, Shuqiang4 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2023 |
| 卷号 | 32页码:5509-5523 |
| ISSN号 | 1057-7149 |
| 关键词 | Ingredient prediction multi-label classification deep learning |
| DOI | 10.1109/TIP.2023.3318958 |
| 英文摘要 | Ingredient prediction has received more and more attention with the help of image processing for its diverse real-world applications, such as nutrition intake management and cafeteria self-checkout system. Existing approaches mainly focus on multi-task food category-ingredient joint learning to improve final recognition by introducing task relevance, while seldom pay attention to making good use of inherent characteristics of ingredients independently. Actually, there are two issues for ingredient prediction. First, compared with fine-grained food recognition, ingredient prediction needs to extract more comprehensive features of the same ingredient and more detailed features of various ingredients from different regions of the food image. Because it can help understand various food compositions and distinguish the differences within ingredient features. Second, the ingredient distributions are extremely unbalanced. Existing loss functions can not simultaneously solve the imbalance between positive-negative samples belonging to each ingredient and significant differences among all classes. To solve these problems, we propose a novel framework named Class-Adaptive Context Learning Network (CACLNet) for ingredient prediction. In order to extract more comprehensive and detailed features, we introduce Ingredient Context Learning (ICL) to reduce the negative impact of complex background in food images and construct internal spatial connections among ingredient regions of food objects in a self-supervised manner, which can strengthen the contacts of the same ingredients through region interactions. In order to solve the imbalance of different classes among ingredients, we propose one novel Class-Adaptive Asymmetric Loss (CAAL) to focus on various ingredient classes adaptively. Besides, considering that the over-suppression of negative samples will over-fit positive samples of those rare ingredients, CAAL alleviates this continuous suppression according to the imbalanced ratios based on gradients while maintaining the contribution of positive samples by lesser suppression. Extensive evaluation on two popular benchmark datasets (Vireo Food-172, UEC Food-100) demonstrates our proposed method achieves the state-of-the-art performance. Further qualitative analysis and visualization show the effectiveness of our method. |
| 资助项目 | National Natural Science Foundation of China[61972378] ; National Natural Science Foundation of China[62125207] ; National Natural Science Foundation of China[U1936203] ; National Natural Science Foundation of China[U19B2040] ; CAAI-Huawei MindSpore Open Fund |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001082264400007 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21109]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Min, Weiqing |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Comp Technol, Suzhou 215002, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Mengjiang,Min, Weiqing,Wang, Zhiling,et al. Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:5509-5523. |
| APA | Luo, Mengjiang,Min, Weiqing,Wang, Zhiling,Song, Jiajun,&Jiang, Shuqiang.(2023).Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,5509-5523. |
| MLA | Luo, Mengjiang,et al."Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):5509-5523. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。