将视觉大模型特征引入红外小目标检测，提升精度无额外计算开销。
Rethinking Infrared Small Target Detection: A Foundation-Driven Efficient Paradigm
- 利用大模型语义先验，动态融合全局语义与任务特征。
- 在不增加推理时间前提下，实现检测精度显著提升。
- 适合追求高精度且轻量部署的红外目标检测应用。
尽管大规模视觉基础模型（VFMs）在多种视觉领域展现出强大泛化能力，但其在单帧红外小目标（SIRST）检测中的潜力仍待探索。为此，我们首次系统性地引入冻结的VFMs表征到SIRST任务中，提出一种基础驱动高效范式（FDEP），可无缝适配现有编码器-解码器结构，并在不增加推理开销的前提下显著提升准确率。具体而言，设计了语义对齐调制融合（SAMF）模块，实现大模型全局语义先验与任务特定特征的动态对齐与深度融合。同时，为避免引入大模型带来的推理延迟，提出基于协同优化的隐式自蒸馏（CO-ISD）策略，通过参数共享与同步反向传播，在主分支与轻量分支间实现隐式语义迁移。此外，为统一碎片化的评估体系，构建了全面的红外小目标检测评估（HSE）指标，从像素级置信度和目标级鲁棒性两个维度进行多阈值积分评价，为公平模型对比提供稳定依据。大量实验表明，采用FDEP框架的SIRST检测网络在多个公开数据集上达到当前最优性能。代码已开源：https://github.com/YuChuang1205/FDEP-Framework。
原文摘要 · Abstract (English)
While large-scale visual foundation models (VFMs) exhibit strong generalization across diverse visual domains, their potential for single-frame infrared small target (SIRST) detection remains largely unexplored. To fill this gap, we systematically introduce the frozen representations from VFMs into the SIRST task for the first time and propose a Foundation-Driven Efficient Paradigm (FDEP), which can seamlessly adapt to existing encoder-decoder-based methods and significantly improve accuracy without additional inference overhead. Specifically, a Semantic Alignment Modulation Fusion (SAMF) module is designed to achieve dynamic alignment and deep fusion of the global semantic priors from VFMs with task-specific features. Meanwhile, to avoid the inference time burden introduced by VFMs, we propose a Collaborative Optimization-based Implicit Self-Distillation (CO-ISD) strategy, which enables implicit semantic transfer between the main and lightweight branches through parameter sharing and synchronized backpropagation. In addition, to unify the fragmented evaluation system, we construct a Holistic SIRST Evaluation (HSE) metric that performs multi-threshold integral evaluation at both pixel-level confidence and target-level robustness, providing a stable and comprehensive basis for fair model comparison. Extensive experiments demonstrate that the SIRST detection networks equipped with our FDEP framework achieve state-of-the-art (SOTA) performance on multiple public datasets. Our code is available at https://github.com/YuChuang1205/FDEP-Framework
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