Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval

• URL: http://arxiv.org/abs/2308.04008v1
• Date: Tue, 8 Aug 2023 03:06:10 GMT
• Title: Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval
• Authors: Yunquan Zhu, Xinkai Gao, Bo Ke, Ruizhi Qiao, Xing Sun
• Abstract summary: Two-stage methods following retrieve-and-rerank paradigm have achieved excellent performance, but their separate local and global modules are inefficient to real-world applications. We propose a mechanism which attentively selects prominent local descriptors and infuse fine-grained semantic relations into the global representation. Our method achieves state-of-the-art single-stage image retrieval performance on benchmarks such as Revisited Oxford and Revisited Paris.
• Score: 11.696941841000985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image retrieval targets to find images from a database that are visually similar to the query image. Two-stage methods following retrieve-and-rerank paradigm have achieved excellent performance, but their separate local and global modules are inefficient to real-world applications. To better trade-off retrieval efficiency and accuracy, some approaches fuse global and local feature into a joint representation to perform single-stage image retrieval. However, they are still challenging due to various situations to tackle, $e.g.$, background, occlusion and viewpoint. In this work, we design a Coarse-to-Fine framework to learn Compact Discriminative representation (CFCD) for end-to-end single-stage image retrieval-requiring only image-level labels. Specifically, we first design a novel adaptive softmax-based loss which dynamically tunes its scale and margin within each mini-batch and increases them progressively to strengthen supervision during training and intra-class compactness. Furthermore, we propose a mechanism which attentively selects prominent local descriptors and infuse fine-grained semantic relations into the global representation by a hard negative sampling strategy to optimize inter-class distinctiveness at a global scale. Extensive experimental results have demonstrated the effectiveness of our method, which achieves state-of-the-art single-stage image retrieval performance on benchmarks such as Revisited Oxford and Revisited Paris. Code is available at https://github.com/bassyess/CFCD.

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