Infrared small target detection (IRSTD) is crucial for surveillance and early-warning, with deployments spanning both single-frame analysis and video-mode tracking. A practical solution should leverage vision foundation models (VFMs) to mitigate infrared data scarcity, while adopting a memory-attention-based temporal propagation framework that unifies single- and multi-frame inference. However, infrared small targets exhibit weak radiometric signals and limited semantic cues, which differ markedly from visible-spectrum imagery. This modality gap makes direct use of semantics-oriented VFMs and appearance-driven cross-frame association unreliable for IRSTD: hierarchical feature aggregation can submerge localized target peaks, and appearance-only memory attention becomes ambiguous, leading to spurious clutter associations. To address these challenges, we propose SPIRIT, a unified and VFM-compatible framework that adapts VFMs to IRSTD via lightweight physics-informed plug-ins. Spatially, PIFR refines features by approximating rank-sparsity decomposition to suppress structured background components and enhance sparse target-like signals. Temporally, PGMA injects history-derived soft spatial priors into memory cross-attention to constrain cross-frame association, enabling robust video detection while naturally reverting to single-frame inference when temporal context is absent. Experiments on multiple IRSTD benchmarks show consistent gains over VFM-based baselines and SOTA performance.

翻译：红外小目标检测在监视与预警系统中至关重要，其部署涵盖单帧分析与视频模式跟踪。实用化解决方案需借助视觉基础模型以缓解红外数据稀缺问题，同时采用基于记忆注意力的时序传播框架来统一单帧与多帧推理。然而，红外小目标具有弱辐射信号与有限语义线索的特征，与可见光谱图像存在显著差异。这种模态差异导致直接使用面向语义的视觉基础模型及外观驱动的跨帧关联对红外小目标检测不可靠：层次化特征聚合可能淹没局部目标峰值，而仅依赖外观的记忆注意力机制易产生模糊性，导致虚假杂波关联。为应对这些挑战，我们提出SPIRIT——一个统一且兼容视觉基础模型的框架，通过轻量级物理信息插件使视觉基础模型适配红外小目标检测任务。空间维度上，PIFR模块通过近似秩稀疏分解优化特征表示，抑制结构化背景成分并增强稀疏类目标信号。时序维度上，PGMA模块将历史衍生的软空间先验注入记忆交叉注意力机制，以约束跨帧关联，在实现鲁棒视频检测的同时，当时序上下文缺失时可自然退化为单帧推理。在多个红外小目标检测基准上的实验表明，该方法相较于基于视觉基础模型的基线取得持续增益，并达到最先进的性能水平。