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Learning and Aggregating Deep Local Descriptors for Instance-Level Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12346)

Abstract

We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the activations of internal components of the network. We demonstrate why such an approach learns local descriptors that work well for image similarity estimation with classical efficient match kernel methods. The experimental validation studies the trade-off between performance and memory requirements of the state-of-the-art image search approach based on match kernels. Compared to existing local descriptors, the proposed ones perform better in two instance-level recognition tasks and keep memory requirements lower. We experimentally show that global descriptors are not effective enough at large scale and that local descriptors are essential. We achieve state-of-the-art performance, in some cases even with a backbone network as small as ResNet18.

Keywords

Deep local descriptors Deep local features Efficient match kernel ASMK Image retrieval Instance-level recognition 

Notes

Acknowledgement

The authors would like to thank Yannis Kalantidis for valuable discussions. This work was supported by MSMT LL1901 ERC-CZ grant. Tomas Jenicek was supported by CTU student grant SGS20/171/OHK3/3T/13.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Visual Recognition Group, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic

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