基于补丁的检索：实例级匹配的实用技术集合
Patch-wise Retrieval: A Bag of Practical Techniques for Instance-level Matching
AI总结：
Patchify通过基于补丁的检索框架实现高效实例级匹配，结合LocScore评估空间正确性，提升检索性能与可解释性。
摘要：
实例级图像检索旨在找到包含与给定查询相同对象的图像，尽管存在大小、位置或外观的变化。为解决这一具有挑战性的任务，我们提出了Patchify，一种简单而有效的基于补丁的检索框架，它在无需微调的情况下提供了高性能、可扩展性和可解释性。Patchify将每个数据库图像划分为少量结构化补丁，并通过将这些局部特征与全局查询描述符进行比较来进行检索，从而实现准确且空间上基于的匹配。为了评估不仅检索准确性，还有空间正确性，我们引入了LocScore，一种定位感知度量，它量化了检索区域是否与目标对象对齐。这使LocScore成为理解并改进检索行为的重要诊断工具。我们在多个基准、骨干网络和区域选择策略上进行了广泛的实验，表明Patchify优于全局方法，并补充了最先进的重排序流水线。此外，我们应用了Product Quantization以实现高效的大规模检索，并强调了在压缩过程中使用信息性特征的重要性，这显著提高了性能。项目网站：https://wons20k.github.io/PatchwiseRetrieval/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
