Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
This work addresses the problem of precise drone control for users through natural language, though it is incremental as it builds on an existing dataset and methods.
The paper tackles the challenge of navigating drones with natural language commands by introducing GeoText-1652, a benchmark dataset with spatial-aware text annotations, and achieves a competitive recall rate compared to existing cross-modality methods.
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 geo-localization 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.