Omni-domain infrared small target detection (Omni-IRSTD) poses formidable challenges, as a single model must seamlessly adapt to diverse imaging systems, varying resolutions, and multiple spectral bands simultaneously. Current approaches predominantly rely on visual-only modeling paradigms that not only struggle with complex background interference and inherently scarce target features, but also exhibit limited generalization capabilities across complex omni-scene environments where significant domain shifts and appearance variations occur. In this work, we reveal a critical oversight in existing paradigms: the neglect of readily available auxiliary metadata describing imaging parameters and acquisition conditions, such as spectral bands, sensor platforms, resolution, and observation perspectives. To address this limitation, we propose the Auxiliary Metadata Driven Infrared Small Target Detector (AuxDet), a novel multimodal framework that is the first to incorporate metadata into the IRSTD paradigm for scene-aware optimization. Through a high-dimensional fusion module based on multi-layer perceptrons (MLPs), AuxDet dynamically integrates metadata semantics with visual features, guiding adaptive representation learning for each individual sample. Additionally, we design a lightweight prior-initialized enhancement module using 1D convolutional blocks to further refine fused features and recover fine-grained target cues. Extensive experiments on the challenging WideIRSTD-Full benchmark demonstrate that AuxDet consistently outperforms state-of-the-art methods, validating the critical role of auxiliary information in improving robustness and accuracy in omni-domain IRSTD tasks. Code is available at https://github.com/GrokCV/AuxDet.

翻译：全领域红外小目标检测（Omni-IRSTD）提出了严峻挑战，因为单一模型必须同时无缝适应不同的成像系统、多变的分辨率以及多个光谱波段。当前方法主要依赖于纯视觉建模范式，不仅难以应对复杂的背景干扰和固有的稀缺目标特征，而且在发生显著域偏移和外观变化的复杂全场景环境中表现出有限的泛化能力。在本工作中，我们揭示了现有范式中的一个关键疏忽：忽视了描述成像参数与采集条件的、易于获取的辅助元数据，例如光谱波段、传感器平台、分辨率与观测视角。为弥补这一局限，我们提出了辅助元数据驱动的红外小目标检测器（AuxDet），这是一种新颖的多模态框架，首次将元数据纳入IRSTD范式以实现场景感知优化。通过基于多层感知机（MLPs）的高维融合模块，AuxDet动态地将元数据语义与视觉特征相融合，指导每个独立样本的自适应表征学习。此外，我们设计了一个采用一维卷积块的轻量级先验初始化增强模块，以进一步精炼融合特征并恢复细粒度的目标线索。在具有挑战性的WideIRSTD-Full基准上进行的大量实验表明，AuxDet始终优于现有最先进方法，验证了辅助信息在提升全领域IRSTD任务鲁棒性与准确性中的关键作用。代码发布于 https://github.com/GrokCV/AuxDet。