用单点标注生成红外小目标伪掩码，提升检测精度。
Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision
- 分步构建伪掩码：从点标注生成包围框，再细化为详细掩码。
- 在三个数据集上，伪掩码平均交并比（IoU）领先第二名4.3%。
- 融合学习与非学习方法，有效减少误检和漏检，适合弱监督场景。
单帧红外小目标（SIRST）检测面临巨大挑战，需在复杂的红外背景杂波中识别微小目标。本文聚焦弱监督范式，通过结合一种无学习方法与基于学习的混合方法，从点级标注生成高质量伪掩码。无学习方法采用逐步流程：从点标注生成目标包围框，进而生成精细伪掩码；混合方法则通过过滤网络预测中的误报、召回漏检，对无学习掩码进行可靠补充。实验表明，所提无学习方法在三个数据集上的平均交并比（IoU）较第二佳无学习方法高出4.3%；而混合学习方法进一步提升伪掩码质量，平均IoU再提高3.4%。
原文摘要 · Abstract (English)
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter. In this paper, we focus on a weakly-supervised paradigm to obtain high-quality pseudo masks from the point-level annotation by integrating a novel learning-free method with the hybrid of the learning-based method. The learning-free method adheres to a sequential process, progressing from a point annotation to the bounding box that encompasses the target, and subsequently to detailed pseudo masks, while the hybrid is achieved through filtering out false alarms and retrieving missed detections in the network's prediction to provide a reliable supplement for learning-free masks. The experimental results show that our learning-free method generates pseudo masks with an average Intersection over Union (IoU) that is 4.3% higher than the second-best learning-free competitor across three datasets, while the hybrid learning-based method further enhances the quality of pseudo masks, achieving an additional average IoU increase of 3.4%.
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