Multi-state Ingredient Recognition via Adaptive Multi-centric Network
文献类型：期刊论文
| 作者 | Wen, Min2,3; Song, Jiajun2,3; Min, Weiqing2,3; Xiao, Weimin1; Han, Lin1; Jiang, Shuqiang2,3 |
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2023-12-14 |
| 页码 | 10 |
| 关键词 | Ingredient recognition intelligent cooking device |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2023.3333935 |
| 英文摘要 | Ingredient recognition has received significant attention due to its numerous industrial applications, such as intelligent retail terminals and intelligent cooking devices. However, ingredient recognition has the following challenges: 1) dynamic changes in the number of categories; 2) greater diversity and regionality of ingredients; and 3) large visual differences among different states of ingredients. In this article, we propose an adaptive multi-centric network (AdMNet) to solve the problem of ingredient recognition. AdMNet is based on the idea of retrieval, which consists of two main parts, the adaptive multi-centric nearest-neighbor central mean (AdM-NCM) classifier, and the context-aware attentional pooling (CAP) module. The AdM-NCM classifier adaptively establishes category-centric vector groups to recognize ingredients via optimizing the minimum clustering variance, where each state of the ingredient has its corresponding centric vector. The CAP module combines contextual information and multiple attention mechanisms. It captures more focused and discriminative features with higher weights assigned to fine-grained features, which results in better feature representation. In addition, we collect a large-scale ingredient dataset, ISIA Ingredient-201 with 201 classes and 100 442 images. To prove the greater robustness and generalization of our method, we compare the metrics in basic scenarios and realistic scenarios with those of other methods. Specifically, the base scenario is the regular setup, and the real scenario is similar to the class incremental learning setup. The experimental results show that our method reaches the state of the art on both basic scenarios and realistic scenarios with small samples. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001129741500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38451]  |
| 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.Versuni, Shanghai 200072, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.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 | Wen, Min,Song, Jiajun,Min, Weiqing,et al. Multi-state Ingredient Recognition via Adaptive Multi-centric Network[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2023:10. |
| APA | Wen, Min,Song, Jiajun,Min, Weiqing,Xiao, Weimin,Han, Lin,&Jiang, Shuqiang.(2023).Multi-state Ingredient Recognition via Adaptive Multi-centric Network.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,10. |
| MLA | Wen, Min,et al."Multi-state Ingredient Recognition via Adaptive Multi-centric Network".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023):10. |
入库方式： OAI收割
来源：计算技术研究所
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