通过在半变异函数上发现难负样本增强地理定位的对比学习
Enhancing Contrastive Learning for Geolocalization by Discovering Hard Negatives on Semivariograms
AI总结：
针对地理定位中对比学习忽视空间依赖导致难负样本与假负样本问题，提出基于半变异函数的空间正则化对比学习策略，集成于GeoCLIP并在OSV5M上提升细粒度定位性能。
摘要：
在全球范围内进行准确且稳健的基于图像的地理定位具有挑战性，原因在于环境多样、视觉场景模糊，以及许多区域缺乏独特的地标。虽然对比学习方法通过对齐街景图像与对应位置之间的特征显示出有前景的性能，但它们忽略了地理空间中潜在的空间依赖性。因此，它们未能解决假负样本的问题——即视觉和地理上都相似但被标记为负样本的图像对，并且难以有效区分难负样本，即视觉相似但地理距离较远的图像对。为解决这一问题，我们提出了一种新颖的空间正则化对比学习策略，该策略整合了半变异函数，这是一种用于建模空间相关性随距离变化的地统计学工具。我们通过将图像在特征空间中的距离与其地理距离相关联来拟合半变异函数，从而捕捉空间相关性中的预期视觉内容。利用拟合的半变异函数，我们将给定空间距离处的预期视觉不相似性定义为参考，以识别难负样本和假负样本。我们将此策略集成到GeoCLIP中，并在OSV5M数据集上进行了评估，结果表明显式建模空间先验能够提升基于图像的地理定位性能，尤其是在更细粒度上。
英文摘要：
Accurate and robust image-based geo-localization at a global scale is challenging due to diverse environments, visually ambiguous scenes, and the lack of distinctive landmarks in many regions. While contrastive learning methods show promising performance by aligning features between street-view images and corresponding locations, they neglect the underlying spatial dependency in the geographic space. As a result, they fail to address the issue of false negatives -- image pairs that are both visually and geographically similar but labeled as negatives, and struggle to effectively distinguish hard negatives, which are visually similar but geographically distant. To address this issue, we propose a novel spatially regularized contrastive learning strategy that integrates a semivariogram, which is a geostatistical tool for modeling how spatial correlation changes with distance. We fit the semivariogram by relating the distance of images in feature space to their geographical distance, capturing the expected visual content in a spatial correlation. With the fitted semivariogram, we define the expected visual dissimilarity at a given spatial distance as reference to identify hard negatives and false negatives. We integrate this strategy into GeoCLIP and evaluate it on the OSV5M dataset, demonstrating that explicitly modeling spatial priors improves image-based geo-localization performance, particularly at finer granularity.
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