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

• URL: http://arxiv.org/abs/2311.12751v4
• Date: Wed, 31 Jul 2024 08:24:16 GMT
• Title: Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
• Authors: Meng Chu, Zhedong Zheng, Wei Ji, Tingyu Wang, Tat-Seng Chua,
• Abstract summary: Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets. We introduce GeoText-1652, a new natural language-guided geo-localization benchmark. This dataset is systematically constructed through an interactive human-computer process.
• Score: 60.645802236700035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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