Proposes weighted aggregation of clusters and self-distillation-driven token pruning to improve both accuracy and efficiency in ViT-based visual place recognition.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
ReaGeo is an end-to-end LLM framework for geocoding that uses geohash text generation, Chain-of-Thought spatial reasoning, and distance-based RL to accurately predict points and regions from explicit and vague queries.
citing papers explorer
-
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.
-
ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs
ReaGeo is an end-to-end LLM framework for geocoding that uses geohash text generation, Chain-of-Thought spatial reasoning, and distance-based RL to accurately predict points and regions from explicit and vague queries.
- InfoGeo: Information-Theoretic Object-Centric Learning for Cross-View Generalizable UAV Geo-Localization