Patch-wise Retrieval: A Bag of Practical Techniques for Instance-level Matching
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arXiv
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| Format: | Preprint |
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2025
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| author | Choi, Wonseok Lim, Sohwi Hyeon-Woo, Nam Ye-Bin, Moon Jeong, Dong-Ju Hwang, Jinyoung Oh, Tae-Hyun |
| author_facet | Choi, Wonseok Lim, Sohwi Hyeon-Woo, Nam Ye-Bin, Moon Jeong, Dong-Ju Hwang, Jinyoung Oh, Tae-Hyun |
| contents | Instance-level image retrieval aims to find images containing the same object as a given query, despite variations in size, position, or appearance. To address this challenging task, we propose Patchify, a simple yet effective patch-wise retrieval framework that offers high performance, scalability, and interpretability without requiring fine-tuning. Patchify divides each database image into a small number of structured patches and performs retrieval by comparing these local features with a global query descriptor, enabling accurate and spatially grounded matching. To assess not just retrieval accuracy but also spatial correctness, we introduce LocScore, a localization-aware metric that quantifies whether the retrieved region aligns with the target object. This makes LocScore a valuable diagnostic tool for understanding and improving retrieval behavior. We conduct extensive experiments across multiple benchmarks, backbones, and region selection strategies, showing that Patchify outperforms global methods and complements state-of-the-art reranking pipelines. Furthermore, we apply Product Quantization for efficient large-scale retrieval and highlight the importance of using informative features during compression, which significantly boosts performance. Project website: https://wons20k.github.io/PatchwiseRetrieval/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12610 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Patch-wise Retrieval: A Bag of Practical Techniques for Instance-level Matching Choi, Wonseok Lim, Sohwi Hyeon-Woo, Nam Ye-Bin, Moon Jeong, Dong-Ju Hwang, Jinyoung Oh, Tae-Hyun Computer Vision and Pattern Recognition Information Retrieval Instance-level image retrieval aims to find images containing the same object as a given query, despite variations in size, position, or appearance. To address this challenging task, we propose Patchify, a simple yet effective patch-wise retrieval framework that offers high performance, scalability, and interpretability without requiring fine-tuning. Patchify divides each database image into a small number of structured patches and performs retrieval by comparing these local features with a global query descriptor, enabling accurate and spatially grounded matching. To assess not just retrieval accuracy but also spatial correctness, we introduce LocScore, a localization-aware metric that quantifies whether the retrieved region aligns with the target object. This makes LocScore a valuable diagnostic tool for understanding and improving retrieval behavior. We conduct extensive experiments across multiple benchmarks, backbones, and region selection strategies, showing that Patchify outperforms global methods and complements state-of-the-art reranking pipelines. Furthermore, we apply Product Quantization for efficient large-scale retrieval and highlight the importance of using informative features during compression, which significantly boosts performance. Project website: https://wons20k.github.io/PatchwiseRetrieval/ |
| title | Patch-wise Retrieval: A Bag of Practical Techniques for Instance-level Matching |
| topic | Computer Vision and Pattern Recognition Information Retrieval |
| url | https://arxiv.org/abs/2512.12610 |