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CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization

Sixing Hu, Mengdan Feng, Rang M. H. Nguyen, Gim Hee Lee, CVPR, 2018

Summary

  1. Propose CVM-Net for cross-view imagebased ground-to-aerial geo-localization.
  2. Contributions:
  • Combine NetVLAD layers with a Siamese network to jointly learn robust representations for cross-view image matching (find the closest match of a query ground image from a given database of geo-tagged satellite images);
  • Introduce a new weighted soft margin ranking loss that not only speeds up the training convergence but also improves the final retrieval accuracy.
  1. Proposed Approach:
  • CNNs (AlexNet/VGG16) are used to extract the local features;
  • Feed the local feature vectors into a NetVLAD layers to get the global descriptor(invariant across large viewpoint changes), where NetVLAD is a trainable deep network version of VLAD. Two strategies are tried, CVM-Net-I (Two independent NetVLADs) and CVM-Net-II (NetVLADs with shared weights);
  • Use a weighted soft-margin triplet loss to train CVM-Nets.
  1. Experiment:

Strengths / Novelties

  • NetVLAD layers to encode local features into a VLAD global descriptor (which is invariant across large viewpoint changes);