Proposes weighted aggregation of clusters and self-distillation-driven token pruning to improve both accuracy and efficiency in ViT-based visual place recognition.
Towards seamless adaptation of pre-trained models for visual place recognition,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.
citing papers explorer
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Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning
Proposes weighted aggregation of clusters and self-distillation-driven token pruning to improve both accuracy and efficiency in ViT-based visual place recognition.
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Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition
RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.