Regressing Transformers for Data-efficient Visual Place Recognition
María Leyva-Vallina Nicola Strisciuglio Nicolai Petkov
Visual place recognition is a critical task in computer vision, especially
for localization and navigation systems. Existing methods often rely on
contrastive learning: image descriptors are trained to have small distance for
similar images and larger distance for dissimilar ones in a latent space.
However, this approach struggles to ensure accurate distance-based image
similarity representation, particularly when training with binary pairwise
labels, and complex re-ranking strategies are required. This work introduces a
fresh perspective by framing place recognition as a regression problem, using
camera field-of-view overlap as similarity ground truth for learning. By
optimizing image descriptors to align directly with graded similarity labels,
this approach enhances ranking capabilities without expensive re-ranking,
offering data-efficient training and strong generalization across several
benchmark datasets.