A2GC：面向局部聚合描述子的几何约束非对称聚合
A2GC: Asymmetric Aggregation with Geometric Constraints for Locally Aggregated Descriptors
把 Sinkhorn 的对称边缘约束改成行列归一化加独立边缘校准的非对称匹配，并用可学习坐标嵌入把空间邻近性注入特征-聚类兼容度；做图像检索/位置识别的人可以直接把这套聚合头替换到现有 VPR 骨干后面。
原文依据 We propose an asymmetric aggregation VPR method with geometric constraints for locally aggregated descriptors, called A2GC-VPR.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉位置识别（VPR）旨在利用视觉线索将查询图像与数据库进行匹配。当前最优方法将深度骨干网络提取的特征聚合为全局描述子。基于最优传输的聚合方法将特征到聚类中心的分配重新表述为传输问题，但标准 Sinkhorn 算法对称地处理源与目标的边缘分布，当图像特征与聚类中心的分布差异显著时，其有效性受到限制。我们提出一种带几何约束的非对称聚合 VPR 方法，用于局部聚合描述子，称为 A2GC-VPR。该方法采用行列归一化平均与独立的边缘校准，实现能够适应视觉位置识别中分布差异的非对称匹配。几何约束通过可学习的坐标嵌入引入，计算与特征相似度融合的兼容性得分，从而促使空间上邻近的特征归入同一聚类，增强空间感知能力。在 MSLS、NordLand 和 Pittsburgh 数据集上的实验结果表明其性能优越，验证了该方法在提升匹配精度与鲁棒性方面的有效性。代码已公开于 https://github.com/CV4RA/A2GC。
英文原文摘要 1302 字符
Visual Place Recognition (VPR) aims to match query images against a database using visual cues. State-of-the-art methods aggregate features from deep backbones to form global descriptors. Optimal transport-based aggregation methods reformulate feature-to-cluster assignment as a transport problem, but the standard Sinkhorn algorithm symmetrically treats source and target marginals, limiting effectiveness when image features and cluster centers exhibit substantially different distributions. We propose an asymmetric aggregation VPR method with geometric constraints for locally aggregated descriptors, called A2GC-VPR. Our method employs row-column normalization averaging with separate marginal calibration, enabling asymmetric matching that adapts to distributional discrepancies in visual place recognition. Geometric constraints are incorporated through learnable coordinate embeddings, computing compatibility scores fused with feature similarities, thereby promoting spatially proximal features to the same cluster and enhancing spatial awareness. Experimental results on MSLS, NordLand, and Pittsburgh datasets demonstrate superior performance, validating the effectiveness of our approach in improving matching accuracy and robustness. Our code is available at https://github.com/CV4RA/A2GC.