Wang, Xin, Wang, Xiang, Wang, Chen et al. (3 more authors) (Accepted: 2019) Discriminative Features Matter: Multi-layer Bilinear Pooling for Camera Localization. In: British Machine Vision Conference, 09-12 Sep 2019. (In Press)
Abstract
Deep learning based camera localization from a single image has been explored recently since these methods are computationally efficient. However, existing methods only provide general global representations, from which an accurate pose estimation can not be reliably derived. We claim that effective feature representations for accurate pose estimation shall be both "informative" (focusing on geometrically meaningful regions) and "discriminative" (accounting for different poses of similar images). Therefore, we propose a novel multi-layer factorized bilinear pooling module for feature aggregation. Specifically, informative features are selected via bilinear pooling, and discriminative features are highlighted via multi-layer fusion. We develop a new network for camera localization using the proposed feature pooling module. The effectiveness of our approach is demonstrated by experiments on an outdoor Cambridge Landmarks dataset and an indoor 7 Scenes dataset. The results show that focusing on discriminative features significantly improves the network performance of camera localization in most cases. Codes will be available soon.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019. The copyright of this document resides with its authors |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Jul 2019 08:10 |
Last Modified: | 15 Feb 2025 00:13 |
Status: | In Press |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148881 |