Beyond Open Vocabulary: Multimodal Prompting for Object Detection in Remote Sensing Images

• URL: http://arxiv.org/abs/2602.01954v1
• Date: Mon, 02 Feb 2026 11:03:01 GMT
• Title: Beyond Open Vocabulary: Multimodal Prompting for Object Detection in Remote Sensing Images
• Authors: Shuai Yang, Ziyue Huang, Jiaxin Chen, Qingjie Liu, Yunhong Wang,
• Abstract summary: Open-vocabulary object detection in remote sensing commonly relies on text-only prompting to specify target categories.<n>In practice, this assumption often breaks down in remote sensing scenarios due to task- and application-specific category semantics.<n>We propose RS-MPOD, a multimodal open-vocabulary detection framework that reformulates category specification beyond text-only prompting.
• Score: 52.7196029918473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary object detection in remote sensing commonly relies on text-only prompting to specify target categories, implicitly assuming that inference-time category queries can be reliably grounded through pretraining-induced text-visual alignment. In practice, this assumption often breaks down in remote sensing scenarios due to task- and application-specific category semantics, resulting in unstable category specification under open-vocabulary settings. To address this limitation, we propose RS-MPOD, a multimodal open-vocabulary detection framework that reformulates category specification beyond text-only prompting by incorporating instance-grounded visual prompts, textual prompts, and their multimodal integration. RS-MPOD introduces a visual prompt encoder to extract appearance-based category cues from exemplar instances, enabling text-free category specification, and a multimodal fusion module to integrate visual and textual information when both modalities are available. Extensive experiments on standard, cross-dataset, and fine-grained remote sensing benchmarks show that visual prompting yields more reliable category specification under semantic ambiguity and distribution shifts, while multimodal prompting provides a flexible alternative that remains competitive when textual semantics are well aligned.

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