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Unifying Deep Local and Global Features for Image Search

2020-03-10 17:40:50
Bingyi Cao, Andre Araujo, Jack Sim

Abstract

Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been studied: global and local image features. In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction. We refer to the new model as DELG, standing for DEep Local and Global features. We leverage lessons from recent feature learning work and propose a model that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads -- requiring only image-level labels. We also introduce an autoencoder-based dimensionality reduction technique for local features, which is integrated into the model, improving training efficiency and matching performance. Experiments on the Revisited Oxford and Paris datasets demonstrate that our jointly learned ResNet-50 based features outperform all previous results using deep global features (most with heavier backbones), and those that further re-rank with local features. Code and models will be released.

Abstract (translated)

URL

https://arxiv.org/abs/2001.05027

PDF

https://arxiv.org/pdf/2001.05027.pdf


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