  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.
