Tiny object detection in remote sensing imagery has attracted significant research interest in recent years. Despite recent progress, achieving balanced detection performance across diverse object scales remains a formidable challenge, particularly in scenarios where dense tiny objects and large objects coexist. Although large foundation models have revolutionized general vision tasks, their application to tiny object detection remains unexplored due to the extreme scale variation and density distribution inherent to remote sensing imagery. To bridge this scale gap, we propose ScaleBridge-Det, to the best of our knowledge, the first large detection framework designed for tiny objects, which could achieve balanced performance across diverse scales through scale-adaptive expert routing and density-guided query allocation. Specifically, we introduce a Routing-Enhanced Mixture Attention (REM) module that dynamically selects and fuses scale-specific expert features via adaptive routing to address the tendency of standard MoE models to favor dominant scales. REM generates complementary and discriminative multi-scale representations suitable for both tiny and large objects. Furthermore, we present a Density-Guided Dynamic Query (DGQ) module that predicts object density to adaptively adjust query positions and numbers, enabling efficient resource allocation for objects of varying scales. The proposed framework allows ScaleBridge-Det to simultaneously optimize performance for both dense tiny and general objects without trade-offs. Extensive experiments on benchmark and cross-domain datasets demonstrate that ScaleBridge-Det achieves state-of-the-art performance on AI-TOD-V2 and DTOD, while exhibiting superior cross-domain robustness on VisDrone.

翻译：近年来，遥感影像中的微小目标检测引起了广泛的研究关注。尽管已取得一定进展，但在不同尺度目标间实现均衡的检测性能仍是一项严峻挑战，尤其是在密集微小目标与大型目标共存的场景中。尽管大型基础模型已革新了通用视觉任务，但由于遥感影像固有的极端尺度变化和密度分布特性，其在微小目标检测中的应用尚未得到探索。为弥合这一尺度差距，我们提出了ScaleBridge-Det——据我们所知，这是首个专为微小目标设计的大型检测框架，通过尺度自适应专家路由和密度引导查询分配，能够在不同尺度间实现均衡的性能。具体而言，我们引入了路由增强混合注意力（REM）模块，该模块通过自适应路由动态选择并融合尺度特定的专家特征，以解决标准MoE模型倾向于主导尺度的问题。REM能够生成适用于微小与大型目标的互补且具有判别性的多尺度表征。此外，我们提出了密度引导动态查询（DGQ）模块，通过预测目标密度自适应调整查询位置与数量，从而实现对不同尺度目标的高效资源分配。所提出的框架使ScaleBridge-Det能够同时优化密集微小目标与通用目标的检测性能，无需权衡取舍。在基准数据集和跨域数据集上的大量实验表明，ScaleBridge-Det在AI-TOD-V2和DTOD数据集上达到了最先进的性能，并在VisDrone数据集上展现出卓越的跨域鲁棒性。