Infrared small target detection (ISTD) under complex backgrounds remains a critical yet challenging task, primarily due to the extremely low signal-to-clutter ratio, persistent dynamic interference, and the lack of distinct target features. While multi-frame detection methods leverages temporal cues to improve upon single-frame approaches, existing methods still struggle with inefficient long-range dependency modeling and insufficient robustness. To overcome these issues, we propose a novel scheme for ISTD, realized through a sparse frames-based spatio-temporal semantic feedback network named FeedbackSTS-Det. The core of our approach is a novel spatio-temporal semantic feedback strategy with a closed-loop semantic association mechanism, which consists of paired forward and backward refinement modules that work cooperatively across the encoder and decoder. Moreover, both modules incorporate an embedded sparse semantic module (SSM), which performs structured sparse temporal modeling to capture long-range dependencies with low computational cost. This integrated design facilitates robust implicit inter-frame registration and continuous semantic refinement, effectively suppressing false alarms. Furthermore, our overall procedure maintains a consistent training-inference pipeline, which ensures reliable performance transfer and increases model robustness. Extensive experiments on multiple benchmark datasets confirm the effectiveness of FeedbackSTS-Det. Code and models are available at: https://github.com/IDIP-Lab/FeedbackSTS-Det.

翻译：复杂背景下的红外小目标检测（ISTD）仍然是一项关键且具有挑战性的任务，这主要归因于极低的信杂比、持续的动态干扰以及目标特征的缺失。尽管多帧检测方法利用时序线索改进了单帧方法，但现有方法仍受限于低效的长程依赖建模和不足的鲁棒性。为克服这些问题，我们提出了一种新颖的ISTD方案，通过一个名为FeedbackSTS-Det的基于稀疏帧的时空语义反馈网络实现。我们方法的核心是一种新颖的时空语义反馈策略，其采用闭环语义关联机制，由成对的前向与后向细化模块组成，在编码器与解码器之间协同工作。此外，两个模块均嵌入了稀疏语义模块（SSM），该模块执行结构化稀疏时序建模，以较低的计算成本捕获长程依赖关系。这种集成设计促进了鲁棒的隐式帧间配准与连续语义细化，有效抑制了虚警。此外，我们的整体流程保持了训练与推理的一致性，确保了可靠的性能迁移并增强了模型的鲁棒性。在多个基准数据集上的大量实验验证了FeedbackSTS-Det的有效性。代码与模型已发布于：https://github.com/IDIP-Lab/FeedbackSTS-Det。