Recent studies show that vision models pre-trained in generic visual learning
tasks with large-scale data can provide useful feature representations for a
wide range of visual perception problems. However, few attempts have been made
to exploit pre-trained foundation models in visual place recognition (VPR). Due
to the inherent difference in training objectives and data between the tasks of
model pre-training and VPR, how to bridge the gap and fully unleash the
capability of pre-trained models for VPR is still a key issue to address. To
this end, we propose a novel method to realize seamless adaptation of
pre-trained models for VPR. Specifically, to obtain both global and local
features that focus on salient landmarks for discriminating places, we design a
hybrid adaptation method to achieve both global and local adaptation
efficiently, in which only lightweight adapters are tuned without adjusting the
pre-trained model. Besides, to guide effective adaptation, we propose a mutual
nearest neighbor local feature loss, which ensures proper dense local features
are produced for local matching and avoids time-consuming spatial verification
in re-ranking. Experimental results show that our method outperforms the
state-of-the-art methods with less training data and training time, and uses
about only 3% retrieval runtime of the two-stage VPR methods with RANSAC-based
spatial verification. It ranks 1st on the MSLS challenge leaderboard (at the
time of submission). The code is released at
this https URL

利用预训练的模型实现可视化地点识别的平滑适应，既全局又局部地适应预训练模型，通过轻量级适配器调整以产生适用于地点匹配的全局和局部特征，并避免耗时的空间验证，在少量数据和训练时间下超过当前最先进方法，并仅使用两阶段地点识别方法运行时间的约 3％，在 MSLS 挑战榜单上排名第 1。