Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data with expensive pixel-level annotations. To reduce the annotation burden, we propose the first method to achieve SIRST detection with single-point supervision. The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters. To handle this issue, we introduce randomness to the clustering process by adding noise to the input images, and then obtain much more reliable pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo" clustering approach, our method can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation. Experiments on four datasets demonstrate that our method can be applied to existing SIRST detection networks to achieve comparable performance with their fully supervised counterparts, which reveals that single-point supervision is strong enough for SIRST detection. Our code will be available at: https://github.com/YeRen123455/SIRST-Single-Point-Supervision.

翻译：单帧红外小目标（SIRST）检测旨在从红外图像的杂波背景中分离出小目标。近年来，基于深度学习方法在SIRST检测中取得了显著性能，但代价是需要大量具有昂贵像素级标注的训练数据。为减轻标注负担，我们提出首个通过单点监督实现SIRST检测的方法。本工作核心思想是利用聚类方法从给定单点标签中恢复每个目标的逐像素掩膜，该方法看似简单实则具有挑战性，因为目标往往不显著且伴随背景杂波。为应对该问题，我们通过向输入图像添加噪声引入聚类过程随机性，然后平均聚类结果获得更可靠的伪掩膜。得益于这种“蒙特卡洛”聚类方法，我们的方法能够精确恢复伪掩膜，从而将任意全监督SIRST检测网络转化为仅需单点标注的弱监督网络。在四个数据集上的实验表明，我们的方法可应用于现有SIRST检测网络，达到与全监督版本相当的性能，这揭示了单点监督对SIRST检测已足够强大。我们的代码将开源至：https://github.com/YeRen123455/SIRST-Single-Point-Supervision。