ProG-Net: Prompted Guidance Network for Visible–Thermal Tiny-Object Detection

Visible–Thermal (VT) object detection provides stable and all-weather perception for autonomous systems and remote-sensing intelligence. However, Tiny-Object Detection (TOD) remains a persistent bottleneck due to severe feature scarcity and distinct cross-modality distribution gaps. At tiny scales, targets offer near-zero textural details and easily vanish during standard downsampling operations. To address these challenges, we propose ProG-Net, a detection framework tailored for visible–thermal tiny-object detection. Specifically, we deploy a frozen Vision-Foundation Model (VFM) backbone to inherit highly generalized pre-trained representations. To counteract feature scarcity, we introduce an auxiliary point-prediction branch governed by a dedicated CM-Point-Head. This mechanism leverages explicit point-prompt guidance to force the frozen encoder to produce highly focused spatial priors, effectively isolating tiny targets from complex background clutter. Furthermore, we design a prompt-guided cross-modality fusion strategy via CM-Det-Neck and CM-Det-Head. This module aggregates and aligns dual-stream representations to compensate for severe modality imbalances. Extensive experiments on RGBT-Tiny and LLVIP benchmarks demonstrate that our proposed ProG-Net generally achieves superior overall performance across multiple evaluation metrics compared to state-of-the-art methods, providing a new insight for the community.