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2018 | OriginalPaper | Chapter

ML-LocNet: Improving Object Localization with Multi-view Learning Network

Authors : Xiaopeng Zhang, Yang Yang, Jiashi Feng

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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Abstract

This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We propose a Multi-view Learning Localization Network (ML-LocNet) by incorporating multi-view learning into a two-phase WSOL model. The multi-view learning would benefit localization due to the complementary relationships among the learned features from different views and the consensus property among the mined instances from each view. In the first phase, the representation is augmented by integrating features learned from multiple views, and in the second phase, the model performs multi-view co-training to enhance localization performance of one view with the help of instances mined from other views, which thus effectively avoids early fitting. ML-LocNet can be easily combined with existing WSOL models to further improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves \(68.6\%\) CorLoc and \(49.7\%\) mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.

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Metadata
Title
ML-LocNet: Improving Object Localization with Multi-view Learning Network
Authors
Xiaopeng Zhang
Yang Yang
Jiashi Feng
Copyright Year
2018
Book
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01218-2

Electronic ISBN: 978-3-030-01219-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01219-9_15

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