AI 中文总结
研究红外小目标检测中有效感受野组织这一架构维度，提出将深度特征变换作为渐进残差校正过程的理论框架，据此构建RFONet，该网络以多网格启发式V循环策略实现分层ERF调度，在多基准测试中性能领先，还提供理论保证及新优化目标。
AI 中文摘要
在这项工作中，我们研究了红外小目标检测中一个此前未被探索的架构维度：特征细化过程中有效感受野（ERF）的组织。与主要改进单个特征算子的现有方法不同，我们认为ERF组织构成了一个独立于感受野设计本身的架构维度，并将深度特征变换表述为渐进残差校正过程，据此建立了ERF调度的理论框架。具体而言，我们揭示ERF细化受两个基本属性支配：尺度 - 频率对应，使不同ERF尺度与不同残差频率特征对齐；非线性非交换性，使不同ERF排序产生根本不同的细化轨迹。这些属性共同表明是ERF组织而非单独的ERF尺度支配细化动态。在此原则指导下，我们提出感受野排序网络（RFONet），它通过仅使用标准3×3卷积的多网格启发式V循环策略实现分层ERF调度。RFONet在多个基准测试中取得了领先性能，参数仅116万，推理速度超157 FPS。除了实证性能，我们的理论分析为在扰动、频移和部分遮挡下的稳定残差细化提供了理论保证，这在卓越的噪声鲁棒性和跨数据集泛化中得到一致体现。最后，我们的框架将ERF组织重新表述为任务相关的优化目标，为未来自适应感受野调度提供了原则基础。
英文摘要
In this work, we investigate a previously unexplored architectural dimension for infrared small target detection: the organization of effective receptive fields (ERFs) during feature refinement. Unlike existing approaches that primarily improve individual feature operators, we argue that ERF organization constitutes an architectural dimension independent of receptive field design itself, and formulate deep feature transformation as a progressive residual correction process, from which a theoretical framework for ERF scheduling is established. Specifically, we reveal that ERF refinement is governed by two fundamental properties: scale-frequency correspondence, which aligns different ERF scales with distinct residual frequency characteristics, and nonlinear non-commutativity, which makes different ERF orderings produce fundamentally different refinement trajectories. Together, these properties show that ERF organization, rather than ERF scale alone, governs refinement dynamics. Guided by these principles, we propose Receptive Field Ordering Network (RFONet), which realizes hierarchical ERF scheduling through a multigrid-inspired V-cycle strategy using only standard $3\times3$ convolutions. RFONet achieves state-of-the-art performance on multiple benchmarks with only 1.16M parameters and over 157 FPS inference speed. Beyond empirical performance, our theoretical analysis provides theoretical guarantees for stable residual refinement under perturbations, frequency shifts, and partial occlusions, which are consistently reflected in superior noise robustness and cross-dataset generalization. Finally, our framework reformulates ERF organization as a task-dependent optimization objective, providing a principled foundation for future adaptive receptive field scheduling.
