Target-Centric Frequency Distillation: Learning Spectrally Aligned Representations for RGB-T Visual Grounding

RGB-Thermal (RGB-T) visual grounding localizes a text-referred object in paired RGB and infrared (IR) images. However, RGB-centric vision-language models provide limited IR semantic modeling, hindering the integration of IR cues into query-conditioned representations. To address this, we propose Target-Centric Frequency Distillation (TCFDistill), a framework for learning spectrally aligned representations for robust grounding. Our key insight is that query-relevant RGB–IR structures exhibit distinct frequency characteristics: low frequencies capture slowly varying, coarse-scale spatial structures, while high frequencies reflect fine-grained local variations. Specifically, we introduce Dual-Frequency Spectral Distillation (DFSD), which refines IR representations through structural knowledge transfer from a pretrained RGB–IR teacher model. DFSD aligns query-conditioned low-frequency target-affinity relations and high-frequency RGB–IR correspondences at matched positions and within local neighborhoods. Furthermore, Spectral Alignment-Guided Representation Modulation (SARM) uses local cross-modal frequency consistency to modulate shared structures and complementary responses between RGB and refined IR representations, producing spatially organized frequency tokens for grounding. Experiments on RefFLIR, RefM${}^3$FD, and RefMFAD demonstrate state-of-the-art performance, surpassing RGBT-VGNet by 2.79% on RefM${}^3$FD Test-B and 4.54% on RefMFAD Test-C. All code will be released upon acceptance.