arXiv (All)
open access
RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, differences in acquisition time and imaging platform between UAV and reference imagery introduce substantial cross-domain appearance and viewpoint shifts, ch
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DroneWAM: Efficient World Action Model for Drone Visual Navigation#1117860
- Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization#1011835
- AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation#972806
- VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training#1131849
- MotionScape: A Motion-Stratified UAV Video Benchmark for World Modeling and Future Video Generation#985043
Record · ID 1011170
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive