  Over the past decade, most methods in visual place recognition (VPR) have
used neural networks to produce feature representations. These networks
typically produce a global representation of a place image using only this
image itself and neglect the cross-image variations (e.g. viewpoint and
illumination), which limits their robustness in challenging scenes. In this
paper, we propose a robust global representation method with cross-image
correlation awareness for VPR, named CricaVPR. Our method uses the attention
mechanism to correlate multiple images within a batch. These images can be
taken in the same place with different conditions or viewpoints, or even
captured from different places. Therefore, our method can utilize the
cross-image variations as a cue to guide the representation learning, which
ensures more robust features are produced. To further facilitate the
robustness, we propose a multi-scale convolution-enhanced adaptation method to
adapt pre-trained visual foundation models to the VPR task, which introduces
the multi-scale local information to further enhance the cross-image
correlation-aware representation. Experimental results show that our method
outperforms state-of-the-art methods by a large margin with significantly less
training time. The code is released at https://github.com/Lu-Feng/CricaVPR.
