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  • GitHub - yanghongji2007 cross_view_localization_L2LTR
    Contribute to yanghongji2007 cross_view_localization_L2LTR development by creating an account on GitHub
  • Cross-view Geo-localization with Layer-to-Layer Transformer
    This paper proposes a novel layer-to-layer Transformer (L2LTR) architecture with self-cross attention mechanism for cross-view geo-localization The following sections detail our problem setting, objective, the L2LTR architecture, and our proposed self-cross attention
  • Cross-view Geo-localization with Layer-to-Layer Transformer
    Unlike existing methods that predominantly fall back on CNN, here we devise a novel layer-to-layer Transformer (L2LTR) that utilizes the properties of self-attention in Transformer to model global dependencies, thus significantly decreasing visual ambiguities in cross-view geo-localization
  • Cross-view Geo-localization with Layer-to-Layer
    In this work, we address the problem of cross-view geo-localization, which estimates the geospatial location of a street view image by matching it with a database of geo-tagged aerial
  • Cross-view Geo-localization with Evolving Transformer
    In this work, we address the problem of cross-view geo-localization, which estimates the geospatial location of a street view image by matching it with a database of geo-tagged aerial images
  • 【层间 Attention - 地理定位匹配】Cross-view Geo-localization . . .
    作者称是第一篇将 Transformer 结构用于 Geo-localization 任务的文章，其实这个也就是应用一下，但有趣的创新点在于作者提出了层间的 Attention 机制。 整体结构如上图，其实很简单，两个 分支结构 一样，由一个 CNN 特征提取 骨架网络和一个编码器组成。
  • Review of cross-view image geolocalization methods
    The evaluation was based on the performances of CVUSA， CVACT， and University-1625 Finally， this review offers an view on the application areas and future development directions of cross-view image geolocalization
  • Benchmarking the Robustness of Cross-view Geo-localization Models
    This paper systematically investigates the impact of corruption data on cross-view geo-localization models, which is a challenge previously overlooked in the context of cross-view geo-localization studies
  • 经典文献阅读之--Cross-view image geo-localization. . . (跨 . . .
    我们提出了一种新颖的变换方法，用于跨视角检索任务，明确地将街景全景图像转换为鸟瞰视图（BEV），有效地弥合了街景与卫星视角之间的差距。 通过设计全景鸟瞰视图共同检索网络（Panorama-BEV Co-Retrieval Network），我们促进了与街景全景和BEV图像的协同卫星检索，超越了BEV的感知限制，全面感知全球布局和局部细节。 我们引入了CVGlobal，一个更接近现实应用场景的全球跨视角检索数据集。 该数据集具有不确定的街景方向，并支持跨区域、跨时间以及街景到地图的检索任务的评估。 我们的方法在多个数据集上进行了广泛评估，并在挑战性的跨区域任务中超越了当前的最先进方法。 图1 跨视角检索的目标是识别与街景全景查询（a）在视觉上最相似的地理参考卫星图像。
  • VimGeo: Efficient Cross-View Geo-Localization with Vision Mamba . . .
    In this paper, we propose VimGeo, an innovative and effi-cient cross-view geo-localization method designed to address geometric spatial misalignments between cross-view images
  • Co-Visual Pattern-Augmented Generative Transformer Learning for . . .
    Extensive experiments on challenging public benchmarks, i e , CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models Our code will be available upon acceptance





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