NDLI: Deep multiple instance learning for image classification and auto-annotation

Content Provider	IEEE Xplore Digital Library
Author	Jiajun Wu Yu Yinan Chang Huang Yu Kai
Copyright Year	2015
Description	Author affiliation: Massachusetts Inst. of Technol., Cambridge, MA, USA (Jiajun Wu) \|\| Inst. of Deep Learning, Baidu, Sunnyvale, CA, USA (Yu Yinan; Chang Huang; Yu Kai)
Abstract	The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection. However, there has been little investigation on how we could build up a deep learning framework in a weakly supervised setting. In this paper, we attempt to model deep learning in a weakly supervised learning (multiple instance learning) framework. In our setting, each image follows a dual multi-instance assumption, where its object proposals and possible text annotations can be regarded as two instance sets. We thus design effective systems to exploit the MIL property with deep learning strategies from the two ends; we also try to jointly learn the relationship between object and annotation proposals. We conduct extensive experiments and prove that our weakly supervised deep learning framework not only achieves convincing performance in vision tasks including classification and image annotation, but also extracts reasonable region-keyword pairs with little supervision, on both widely used benchmarks like PASCAL VOC and MIT Indoor Scene 67, and also a dataset for image-and patch-level annotations.
Starting Page	3460
Ending Page	3469
File Size	1193731
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298968
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Proposals Visualization Feature extraction Noise measurement Neural networks Supervised learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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