Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks

In this paper, we propose an end-to-end Attention-Block network for image retrieval (ABIR), which greatly increases the retrieval accuracy without human annotations like bounding boxes. Specifically, our network utilizes coarse-scale feature fusion, which generates the attentive local features via combining the information from different intermediate layers. Detailed feature information is extracted with the application of two attention blocks. Extensive experiments show that our method outperforms the state-of-the-art by a significant margin on four public datasets for image retrieval tasks.