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We propose an efficient method to learn deep local descriptors forinstance-level recognition. The training only requires examples of positive andnegative image pairs and is performed as metric learning of sum-pooled globalimage descriptors. At inference, the local descriptors are provided by theactivations of internal components of the network. We demonstrate why such anapproach learns local descriptors that work well for image similarityestimation with classical efficient match kernel methods. The experimentalvalidation studies the trade-off between performance and memory requirements ofthe state-of-the-art image search approach based on match kernels. Compared toexisting local descriptors, the proposed ones perform better in twoinstance-level recognition tasks and keep memory requirements lower. Weexperimentally show that global descriptors are not effective enough at largescale and that local descriptors are essential. We achieve state-of-the-artperformance, in some cases even with a backbone network as small as ResNet18.

Learning and aggregating deep local descriptors for instance-level recognition