Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching

doi:10.1007/978-3-031-73247-8_13

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Residential College	false
Status	即將出版Forthcoming
	Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
	Chu, Meng 1; Zheng, Zhedong2 ; Ji, Wei 1; Wang, Tingyu 3; Chua, Tat Seng 1
	2025
Source Publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15069 LNCS
Pages	213-231
Abstract	Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets and the stringent precision requirements for aligning visual and textual data. To address this pressing need, we introduce GeoText-1652, a new natural language-guided geolocalization benchmark. This dataset is systematically constructed through an interactive human-computer process leveraging Large Language Model (LLM) driven annotation techniques in conjunction with pre-trained vision models. GeoText-1652 extends the established University-1652 image dataset with spatial-aware text annotations, thereby establishing one-to-one correspondences between image, text, and bounding box elements. We further introduce a new optimization objective to leverage fine-grained spatial associations, called blending spatial matching, for region-level spatial relation matching. Extensive experiments reveal that our approach maintains a competitive recall rate comparing other prevailing cross-modality methods. This underscores the promising potential of our approach in elevating drone control and navigation through the seamless integration of natural language commands in real-world scenarios.
Keyword	Drone Navigation Geolocalization Spatial Relation Matching Text Guidance
DOI	10.1007/978-3-031-73247-8_13
URL	View the original
Language	英語English
Scopus ID	2-s2.0-85210022886
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science View Full-Text via Scopus
Citation statistics
Document Type	Conference paper
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation	1.School of Computing, National University of Singapore, Singapore, Singapore 2.FST and ICI, University of Macau, Macao 3.School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
Recommended Citation GB/T 7714	Chu, Meng,Zheng, Zhedong,Ji, Wei,et al. Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching[C], 2025, 213-231.
APA	Chu, Meng., Zheng, Zhedong., Ji, Wei., Wang, Tingyu., & Chua, Tat Seng (2025). Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15069 LNCS, 213-231.

Files in This Item:
There are no files associated with this item.

If you have any objections to this item, please fill out the form below and the administrator will contact you as soon as possible.
Content:
Email：	*
Affiliation No.
Verification Code:	Refresh

Any comments and suggestions are welcomed.
Title:	*
Content:
Email：	*
Verification Code:	Refresh