DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features

• URL: http://arxiv.org/abs/2108.02927v1
• Date: Fri, 6 Aug 2021 03:14:09 GMT
• Title: DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
• Authors: Min Yang, Dongliang He, Miao Fan, Baorong Shi, Xuetong Xue, Fu Li, Errui Ding, Jizhou Huang
• Abstract summary: We propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. The whole framework is end-to-end differentiable and can be trained with image-level labels.
• Score: 42.62089148690047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective single-stage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.

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