Knowledge Commons of Institute of Automation，CAS
| Product image recognition with guidance learning and noisy supervision | |
| Li, Qing1,2; Peng, Xiaojiang2; Cao, Liangliang3; Du, Wenbin2; Xing, Hao4; Qiao, Yu2; Peng, Qiang1 | |
| Source Publication | COMPUTER VISION AND IMAGE UNDERSTANDING
 |
| ISSN | 1077-3142 |
| 2020-07-01 | |
| Volume | 196Pages:7 |
| Corresponding Author | Peng, Xiaojiang(xj.peng@siat.ac.cn) |
| Abstract | This paper considers to recognize products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category diversifies, noisy labels, etc. We address this problem by two contributions. First, we introduce a novel large-scale product image dataset, termed as Product-90. Instead of collecting product images by laborious and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers. Labels are assigned automatically by the categories of e-commerce websites. Totally the Product-90 consists of more than 140K images with 90 categories. Due to the fact that consumers may upload unrelated images, it is inevitable that our Product-90 introduces noisy labels. As the second contribution, we develop a simple yet efficient guidance learning (GL) method for training convolutional neural networks (CNNs) with noisy supervision. The GL method first trains an initial teacher network with the full noisy dataset, and then trains a target/student network with both large-scale noisy set and small manually-verified clean set in a multi-task manner. Specifically, in the stage of student network training, the large-scale noisy data is supervised by its guidance knowledge which is the combination of its given noisy label and the soften label from the teacher network. We conduct extensive experiments on our Products-90 and four public datasets, namely Food101, Food-101N, Clothing1M and synthetic noisy CIFAR-10. Our guidance learning method achieves performance superior to state-of-the-art methods on these datasets. |
| DOI | 10.1016/j.cviu.2020.102963 |
| WOS Keyword | CLASSIFICATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Science and Technology Service Network Initiative of Chinese Academy of Sciences[KFJ-STS-QYZX092] ; Guangdong Special Support Program[2016TX03X276] ; National Natural Science Foundation of China[U1813218] ; National Natural Science Foundation of China[U1713208] ; Shenzhen Basic Research Program[JCYJ20170818164704758] ; Shenzhen Basic Research Program[CXB201104220032A] ; Joint Lab of CAS-HK |
| Funding Organization | Science and Technology Service Network Initiative of Chinese Academy of Sciences ; Guangdong Special Support Program ; National Natural Science Foundation of China ; Shenzhen Basic Research Program ; Joint Lab of CAS-HK |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000540215400002 |
| Publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/39809 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Peng, Xiaojiang |
| Affiliation | 1.Southwest Jiaotong Univ, Dept Sch Informat Sci & Technol, Chengdu, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Comp Vis & Pattern Recognit, Shenzhen, Peoples R China 3.Univ Massachusetts, Coll Informat & Comp Sci, Amherst, MA 01003 USA 4.Vipshop Inc, Guangzhou, Guangdong, Peoples R China |
| Recommended Citation GB/T 7714 | Li, Qing,Peng, Xiaojiang,Cao, Liangliang,et al. Product image recognition with guidance learning and noisy supervision[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,196:7. |
| APA | Li, Qing.,Peng, Xiaojiang.,Cao, Liangliang.,Du, Wenbin.,Xing, Hao.,...&Peng, Qiang.(2020).Product image recognition with guidance learning and noisy supervision.COMPUTER VISION AND IMAGE UNDERSTANDING,196,7. |
| MLA | Li, Qing,et al."Product image recognition with guidance learning and noisy supervision".COMPUTER VISION AND IMAGE UNDERSTANDING 196(2020):7. |
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