CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
CVPR 2024(2024)
摘要
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
self-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. Our method achieves 94.5
features. The code is released at https://github.com/Lu-Feng/CricaVPR.
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