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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.","url_abs":"https://arxiv.org/abs/2108.02927v2","url_pdf":"https://arxiv.org/pdf/2108.02927v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dolg-single-stage-image-retrieval-with-deep","repo_url":"https://github.com/feymanpriv/DOLG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dolg-single-stage-image-retrieval-with-deep","repo_url":"https://github.com/feymanpriv/DOLG-paddle","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok"}},{"paper_slug":"dolg-single-stage-image-retrieval-with-deep","repo_url":"https://github.com/Shiro-LK/python-DOLG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dolg-single-stage-image-retrieval-with-deep","repo_url":"https://github.com/dongkyuk/DOLG-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dolg-single-stage-image-retrieval-with-deep","repo_url":"https://github.com/innat/DOLG-TensorFlow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"dolg","method_name":"DOLG"}],"datasets_introduced":[],"methods_introduced":[{"slug":"dolg","name":"DOLG","full_name":"Deep Orthogonal Fusion of Local and Global Features"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.02927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02927"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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