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:
this https URL

本文提出了一种新方法，使用单点监督来缓解红外小目标检测中昂贵训练数据的注释负担，并通过引入聚类方法的随机性和 “蒙特卡罗” 聚类方法来精确恢复拟似掩码，从而将任意完全监督的小目标检测网络转变为仅具有单点注释的弱监督网络。实验证明，该方法可以达到与完全监督网络相当的性能，揭示了单点监督的强大性。