Object detectors emerge in Deep Scene CNNs
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Torralba_Object detectors.pdf
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7.03 MB
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Adobe PDF
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Author(s)
Zhou, Bolei
Khosla, Aditya
Lapedriza Garcia, Agata
Oliva, Aude
Torralba, Antonio
Date Issued
May 2015
Journal
Proceedings of the 2015 International Conference on Learning Representations
Citation
Bolei, Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Object detectors emerge in Deep Scene CNNs." 2015 International Conference on Learning Representations, May 7-9, 2015.
Version
Author's final manuscript
Abstract
With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures. Here we show that object detectors emerge from training CNNs to perform scene classification. As scenes are composed of objects, the CNN for scene classification automatically discovers meaningful objects detectors, representative of the learned scene categories. With object detectors emerging as a result of learning to recognize scenes, our work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having been explicitly taught the notion of objects.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://www.iclr.cc/doku.php?id=iclr2015:main#conference_schedule
