Title: SAGE: Spatial-visual Adaptive Graph Exploration for Visual Place Recognition
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Visual Place Recognition, Geo-Localization, Graph Mining, Global Descriptor

Abstract: Visual Place Recognition (VPR) requires robust retrieval of geotagged images despite large appearance, viewpoint, and environmental variation. 
Prior methods focus on descriptor fine-tuning or fixed sampling strategies yet neglect the dynamic interplay between spatial context and visual similarity during training.
We present SAGE (Spatial-visual Adaptive Graph Exploration), a unified training pipeline that enhances granular spatial–visual discrimination by jointly improving local feature aggregation, organize samples during training, and hard sample mining. 
We introduce a lightweight Soft Probing module that learns residual weights from training data for patch descriptors before bilinear aggregation, boosting distinctive local cues.
During training we reconstruct an online geo–visual graph that fuses geographic proximity and current visual similarity so that candidate neighborhoods reflect the evolving embedding landscape. 
To concentrate learning on the most informative place neighborhoods, we seed clusters from high-affinity anchors and iteratively expand them with a greedy weighted clique expansion sampler.
Implemented with a frozen DINOv2 backbone and parameter-efficient fine-tuning, SAGE achieves SOTA across eight benchmarks. It attains 98.9\%, 95.8\%, 94.5\%, and 96.0\% Recall@1 on SPED, Pitts30k-test, MSLS-val, and Nordland, respectively. Notably, our method obtains 100\% Recall@10 on SPED only using 4096D global descriptors.
Code and pretrained weights will be publicly available.

TL;DR: A novel Visual Place Recognition approach incorporating graph mining and local feature enhancement, achieving SoTA with parameter efficiency fine-tuning and compact descriptors.