论文标题
补丁 - 网格+：学习补丁描述符和加权匹配策略，以识别位置
Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition
论文作者
论文摘要
在城市或室内场景等场景中，视觉位置识别（VPR）是一个主要挑战。现有的使用全局描述符的VPR方法很难在场景中捕获本地特定区域（LSR），因此在这种情况下容易陷入本地化困惑。结果，找到对位置识别至关重要的LSR成为关键。为了应对这一挑战，我们引入了Patch-Netvlad+，它的灵感来自基于补丁的VPR研究。我们的方法提出了一种微调策略，并具有三重损失，以使NetVlad适合提取贴片级描述符。此外，与现有的方法不同地处理图像中所有补丁的方法不同，我们的方法提取了LSR的补丁，在整个数据集中，它们的频率较低，并且通过向其分配适当的权重来使其在VPR中起重要作用。匹兹堡30k和Tokyo247数据集的实验表明，与现有基于补丁的方法相比，我们的方法最高可提高6.35％的性能。
Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific regions (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSR that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 6.35\% performance improvement than existing patch-based methods.
