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文章背景与核心概要

跨视角视频地理定位（CVG）旨在通过检索带地理标签的航拍图像来定位地面视角视频。然而，传统方法严重依赖固定长度的输入以及事后求精机制，这限制了其在面对局部或动态观测时的在线定位能力。

为了克服这些局限性，本文提出了渐进式跨视角视频地理定位（PCVG）这一面向实际部署的扩展与评估协议，支持在不同时间预算下的在线定位、前缀式推理、随机起点评估以及带中断的长距离定位。为此，作者推出了 X\(^2\)Localizer——一种新颖的跨粒度对齐框架，该框架利用预算依赖的非对称目标，对全局“前缀-航拍”检索以及“token聚合的帧-航拍图块”匹配进行联合监督。此外，本文还设计了滑动窗口重新定位（SWRL）策略，能够动态刷新候选区域以实现故障恢复和长距离部署，而无需重新处理整个视频序列。实验表明，该方法在保持传统全视频性能的同时，大幅提升了早期定位和单帧粗检索的准确性，缩小了基准评估与真实世界部署之间的差距。

X\(^2\)Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization

X\(^2\)Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization

Summary

Summary

X\(^2\)Localizer is a novel cross-grained alignment framework designed for Progressive Cross-view Video Geo-localization (PCVG)—a deployment-oriented evaluation protocol that addresses the limitations of traditional Cross-view Video Geo-localization (CVG) methods. While conventional approaches rely on fixed-length inputs and post-hoc refinement, X\(^2\)Localizer supports online-oriented localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions.

X\(^2\)Localizer is a novel cross-grained alignment framework designed for Progressive Cross-view Video Geo-localization (PCVG)—a deployment-oriented evaluation protocol that addresses the limitations of traditional Cross-view Video Geo-localization (CVG) methods. While conventional approaches rely on fixed-length inputs and post-hoc refinement, X\(^2\)Localizer supports online-oriented localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions.

Key contributions and technical highlights include: - Cross-grained Alignment Framework: Jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching utilizing a budget-dependent asymmetric objective. - Sliding-Window Re-Localization (SWRL) Strategy: Dynamically refreshes candidate regions to enable failure recovery and long-range deployment without requiring full-sequence reprocessing. - Superior Empirical Performance: Preserves conventional full-video performance while drastically improving early localization, single-frame coarse retrieval, and robustness under random-start and long-distance scenarios.

Key contributions and technical highlights include: - Cross-grained Alignment Framework: Jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching utilizing a budget-dependent asymmetric objective. - Sliding-Window Re-Localization (SWRL) Strategy: Dynamically refreshes candidate regions to enable failure recovery and long-range deployment without requiring full-sequence reprocessing. - Superior Empirical Performance: Preserves conventional full-video performance while drastically improving early localization, single-frame coarse retrieval, and robustness under random-start and long-distance scenarios.

Paper Metadata

Paper Metadata

  • arXiv ID: arXiv:2608.16658 [cs.CV]
  • Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
  • Accepted Venue: The 37th British Machine Vision Conference (BMVC 2026)
  • Timeline: Submitted on August 17, 2026; Last revised on August 27, 2026 (v2).
  • Authors:
  • Zichao Zeng
  • Weijia Fan
  • Yufan Chen
  • June Moh Goo
  • Junwei Zheng
  • Ruiping Liu
  • Kunyu Peng
  • Jiaming Zhang
  • Rainer Stiefelhagen
  • Jan Boehm
  • arXiv ID: arXiv:2608.16658 [cs.CV]
  • Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
  • Accepted Venue: The 37th British Machine Vision Conference (BMVC 2026)
  • Timeline: Submitted on August 17, 2026; Last revised on August 27, 2026 (v2).
  • Authors:
  • Zichao Zeng
  • Weijia Fan
  • Yufan Chen
  • June Moh Goo
  • Junwei Zheng
  • Ruiping Liu
  • Kunyu Peng
  • Jiaming Zhang
  • Rainer Stiefelhagen
  • Jan Boehm

Abstract

Abstract

Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations.

Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations.

In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X\(^2\)Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching with a budget-dependent asymmetric objective.

In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X\(^2\)Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching with a budget-dependent asymmetric objective.

Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X\(^2\)Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X\(^2\)Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.

Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X\(^2\)Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X\(^2\)Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.

Additional Resources & Full-Text Links

Additional Resources & Full-Text Links