Product image recognition with guidance learning and noisy supervision
Li, Qing1,2; Peng, Xiaojiang2; Cao, Liangliang3; Du, Wenbin2; Xing, Hao4; Qiao, Yu2; Peng, Qiang1
Corresponding AuthorPeng, Xiaojiang(
AbstractThis 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.
Indexed BySCI
Funding ProjectScience 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 OrganizationScience 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 AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000540215400002
Citation statistics
Document Type期刊论文
Corresponding AuthorPeng, Xiaojiang
Affiliation1.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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Qing]'s Articles
[Peng, Xiaojiang]'s Articles
[Cao, Liangliang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Qing]'s Articles
[Peng, Xiaojiang]'s Articles
[Cao, Liangliang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Qing]'s Articles
[Peng, Xiaojiang]'s Articles
[Cao, Liangliang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.