发表机构
University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视觉地点识别中固定聚合抑制新环境区分性的问题，提出可靠性引导的免训练聚合方法TFA，利用跨码本一致性与谱统计校准检索，在多个基准上显著提升Recall@1。
AI 中文摘要
冻结的视觉基础模型为视觉地点识别提供可迁移特征，但固定的聚合方式可能抑制新环境中有用的区分信息。我们提出TFA，一种可靠性引导的免训练聚合方法，既不需要地点标签，也不需要特定任务权重更新。我们的关键观察是，可复现的检索不必具有区分性：独立的码本可以一致地检索少数数据库枢纽。TFA结合跨码本一致性、检索覆盖率和谱统计量，以控制残差分配、谱整形和全局特征融合。其谱核在零干预时精确恢复原始描述符相似性。仅数据库的TFA在访问查询前固定其规则；TFA-C64使用64张不重叠的无标签目标图像来校准后续查询的检索。在20个地面协议中，使用固定的DINOv2-B骨干和匹配分辨率，仅数据库的TFA在MSLS-val上将Recall@1比AnyLoc提升17.39个百分点，在SPED上提升9.55个百分点。C64缓解了驾驶环境中仅数据库校准的失败。在8个空中/跨视图协议中，TFA在16个DINOv2/DINOv3骨干-协议组合中的14个上取得最高的Recall@1，与所比较的免训练头部相比。在单独的原生系统比较中，基于DINOv2-G的TFA-C64在Pitts30k上达到91.46%的Recall@1，在VPAIR上达到76.29%，在所有五个基准上优于所展示的免训练比较器。这些结果表明，可靠性引导的聚合可以从冻结表示中恢复额外的检索能力，为地点监督稀缺的新环境提供实用基线。
英文摘要
Frozen visual foundation models provide transferable features for visual place recognition, but fixed aggregation can suppress useful distinctions in new environments. We introduce TFA, a reliability-guided, training-free aggregation method requiring neither place labels nor task-specific weight updates. Our key observation is that reproducible retrieval need not be discriminative: independent codebooks can consistently retrieve a few database hubs. TFA combines cross-codebook agreement, retrieval coverage, and spectral statistics to control residual assignment, spectral shaping, and global-feature fusion. Its spectral kernel exactly recovers original descriptor similarity at zero intervention. Database-only TFA fixes its rules before accessing queries; TFA-C64 uses 64 disjoint unlabeled target images to calibrate retrieval for subsequent queries. Across 20 ground protocols with a fixed DINOv2-B backbone and matched resolution, database-only TFA improves Recall@1 over AnyLoc by 17.39 percentage points on MSLS-val and 9.55 on SPED. C64 mitigates failures of database-only calibration in driving environments. Across eight aerial/cross-view protocols, TFA achieves the highest Recall@1 among compared training-free heads in 14 of 16 DINOv2/DINOv3 backbone-protocol combinations. In a separate native-system comparison, DINOv2-G-based TFA-C64 reaches 91.46% Recall@1 on Pitts30k and 76.29% on VPAIR, outperforming the displayed training-free comparators on all five benchmarks. These results show that reliability-guided aggregation can recover additional retrieval capability from frozen representations, providing a practical baseline for new environments with scarce place supervision.
Comments26 pages, 5 figures, 9 tables, including appendices
