This work explores attention models to weight the contribution of local
convolutional representations for the instance search task. We present a
retrieval framework based on bags of local convolutional features (BLCF) that
benefits from saliency weighting to build an efficient image representation.
The use of human visual attention models (saliency) allows significant
improvements in retrieval performance without the need to conduct region
analysis or spatial verification, and without requiring any feature fine
tuning. We investigate the impact of different saliency models, finding that
higher performance on saliency benchmarks does not necessarily equate to
improved performance when used in instance search tasks. The proposed approach
outperforms the state-of-the-art on the challenging INSTRE benchmark by a large
margin, and provides similar performance on the Oxford and Paris benchmarks
compared to more complex methods that use off-the-shelf representations. The
source code used in this project is available at
this https URL

通过注意力模型对局部卷积特征进行加权，提出了一种基于局部卷积特征包（BLCF）的检索框架，该框架利用人类视觉注意模型（显着性）构建了高效的图像表示。该方法在实例搜索任务中取得了比先前状态的最优性能的显著提高，并在 Oxford 和 Paris 基准测试上提供了与使用现成表示方法的更复杂方法相似的性能。