It has been shown that image descriptors extracted by convolutional neural
networks (CNNs) achieve remarkable results for retrieval problems. In this
paper, we apply attention mechanism to CNN, which aims at enhancing more
relevant features that correspond to important keypoints in the input image.
The generated attention-aware features are then aggregated by the previous
state-of-the-art generalized mean (GeM) pooling followed by normalization to
produce a compact global descriptor, which can be efficiently compared to other
image descriptors by the dot product. An extensive comparison of our proposed
approach with state-of-the-art methods is performed on the new challenging
ROxford5k and RParis6k retrieval benchmarks. Results indicate significant
improvement over previous work. In particular, our attention-aware GeM (AGeM)
descriptor outperforms state-of-the-art method on ROxford5k under the `Hard'
evaluation protocal.

本文介绍了如何通过应用注意机制和广义平均池化将卷积神经网络提取的图像描述符聚合为全局描述符，提高检索结果，并在 ROxford5k 和 RParis6k 这两个检索基准上获得了显着的性能提升。