FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2601.14690v1
• Date: Wed, 21 Jan 2026 06:06:36 GMT
• Title: FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Infrared Small Target Detection
• Authors: Yian Huang, Qing Qin, Aji Mao, Xiangyu Qiu, Liang Xu, Xian Zhang, Zhenming Peng,
• Abstract summary: Infrared small detection target (ISTD) under complex backgrounds remains a challenging task.<n>Existing methods still struggle with inefficient long-range dependency modeling.<n>We propose a novel scheme for ISTD detection through a sparse semantic-temporal feedback network.
• Score: 7.648318265124807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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