Attribute-Prompting Multi-Modal Object Reasoning Transformer for Remote Sensing Visual Grounding
Heqian Qiu, Lanxiao Wang, Minjian Zhang, Taijin Zhao, Hongliang Li · 2024
Remote sensing visual grounding (RSVG) task aims to locate the particular object in a remote sensing image referred to a natural language expression, which requires to precisely fuse and align features from different modalities. However, existing methods usually use object-based multi-modal fusion, which is limited to capturing the detailed object characteristics in remote sensing images, resulting in object confusion with similar objects. To address this problem, we propose an attribute-prompting multi-modal object reasoning network for RSVG. Specifically, we first develop a learnable attribute prompter to adaptively explore diverse and rich attribute information according to common object characteristics in RS. With the help of attribute prompts, we design an attribute-prompting multi-modal fusion encoder to build fine-grained interactive and alignment between the visual and language features to avoid object confusion. Furthermore, we design a multi-modal progressive object reasoning decoder to gradually query more comprehensive object features for accurate object localization. Experimental results demonstrate that the proposed method achieves significant improvements.