TVI-MFAN: A Text–Visual Interaction Multilevel Feature Alignment Network for Visual Grounding in Remote Sensing
Hao Chi, Weiwei Qin, Xingyu Chen, Wenxin Guo, Baiwei An · Remote Sensing · 2025
Visual grounding for remote sensing (RSVG) focuses on localizing specific objects in remote sensing (RS) imagery based on linguistic expressions. Existing methods typically employ pre-trained models to locate the referenced objects. However, due to the insufficient capability of cross-modal interaction and alignment, the extracted visual features may suffer from semantic drift, limiting the performance of RSVG. To address this, the article introduces a novel RSVG framework named the text–visual interaction multilevel feature alignment network (TVI-MFAN), which leverages a text–visual interaction attention (TVIA) module to dynamically generate adaptive weights and biases at both spatial and channel dimensions, enabling the visual feature to focus on relevant linguistic expressions. Additionally, a multilevel feature alignment network (MFAN) aggregates contextual information by using cross-modal alignment to enhance features and suppress irrelevant regions. Experiments demonstrate that the proposed method achieves 75.65% and 80.24% (2.42% and 3.1% absolute improvement) accuracy on the OPT-RSVG and DIOR-RSVG dataset, validating its effectiveness.