Attend in groups: a weakly-supervised deep learning framework for learning from web data

Bohan Zhuang, Lingqiao Liu, Yao Li, Chunhua Shen, Ian Reid

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

25 Citations (Scopus)

Abstract

Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and timeconsuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervised deep learning framework which is robust to the label noise in Web images. The proposed framework relies on two unified strategies - random grouping and attention - to effectively reduce the negative impact of noisy web image annotations. Specifically, random grouping stacks multiple images into a single training instance and thus increases the labeling accuracy at the instance level. Attention, on the other hand, suppresses the noisy signals from both incorrectly labeled images and less discriminative image regions. By conducting intensive experiments on two challenging datasets, including a newly collected fine-grained dataset withWeb images of different car models, 1, the superior performance of the proposed methods over competitive baselines is clearly demonstrated.

Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
EditorsYanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2915-2924
Number of pages10
ISBN (Electronic)9781538604571
ISBN (Print)9781538604588
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America
Duration: 21 Jul 201726 Jul 2017
http://cvpr2017.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/8097368/proceeding (Proceedings)

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2017
Abbreviated titleCVPR 2017
CountryUnited States of America
CityHonolulu
Period21/07/1726/07/17
Internet address

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