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• W2895038424 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." @default.
• W2895038424 created "2018-10-12" @default.
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• W2895038424 date "2018-01-01" @default.
• W2895038424 modified "2023-09-23" @default.
• W2895038424 title "ML-LocNet: Improving Object Localization with Multi-view Learning Network" @default.
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• W2895038424 doi "https://doi.org/10.1007/978-3-030-01219-9_15" @default.
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