RA-LWLM uses retrieval from per-scene databases and in-context learning with a frozen foundation model to achieve cross-scene wireless localization without retraining.
OpenNavMap: Structure-free topomet- ric mapping via large-scale collaborative localization,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
G2G attaches three small trainable modules to frozen backbones and reports state-of-the-art inter-group pose accuracy on four datasets spanning simulation, real cross-season, and sim-to-real transfer using only relative-pose supervision.
citing papers explorer
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RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models
RA-LWLM uses retrieval from per-scene databases and in-context learning with a frozen foundation model to achieve cross-scene wireless localization without retraining.
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G2G: Exploiting Intra-Group Geometry for Inter-Group Pose Estimation
G2G attaches three small trainable modules to frozen backbones and reports state-of-the-art inter-group pose accuracy on four datasets spanning simulation, real cross-season, and sim-to-real transfer using only relative-pose supervision.