Infrared Small Target Detection is a challenging task to separate small
targets from infrared clutter background. Recently, deep learning paradigms
have achieved promising results. However, these data-driven methods need plenty
of manual annotation. Due to the small size of infrared targets, manual
annotation consumes more resources and restricts the development of this field.
This letter proposed a labor-efficient and cursory annotation framework with
level set, which obtains a high-quality pseudo mask with only one cursory
click. A variational level set formulation with an expectation difference
energy functional is designed, in which the zero level contour is intrinsically
maintained during the level set evolution. It solves the issue that zero level
contour disappearing due to small target size and excessive regularization.
Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach
achieves superior performance. Code is available at
this https URL

这篇论文提出了一种劳动效率高且粗略标注框架，通过使用级数集来获得仅需一次粗略点击即可得到高质量伪掩码，解决了因小目标尺寸和过度正则化导致的零水平轮廓消失的问题，实验表明该方法在 NUAA-SIRST 和 IRSTD-1k 数据集上具有优异的性能。

点击 - 遮罩：基于水平集的红外小目标检测的高效标注框架

Click on Mask: A Labor-efficient Annotation Framework with Level Set for  Infrared Small Target Detection

Consider the problem of estimating the $\gamma$-level set
$G^*_{\gamma}=\{x:f(x)\geq\gamma\}$ of an unknown $d$-dimensional density
function $f$ based on $n$ independent observations $X_1,...,X_n$ from the
density. This problem has been addressed under global error criteria related to
the symmetric set difference. However, in certain applications a spatially
uniform mode of convergence is desirable to ensure that the estimated set is
close to the target set everywhere. The Hausdorff error criterion provides this
degree of uniformity and, hence, is more appropriate in such situations. It is
known that the minimax optimal rate of error convergence for the Hausdorff
metric is $(n/\log n)^{-1/(d+2\alpha)}$ for level sets with boundaries that
have a Lipschitz functional form, where the parameter $\alpha$ characterizes
the regularity of the density around the level of interest. However, the
estimators proposed in previous work are nonadaptive to the density regularity
and require knowledge of the parameter $\alpha$. Furthermore, previously
developed estimators achieve the minimax optimal rate for rather restricted
classes of sets (e.g., the boundary fragment and star-shaped sets) that
effectively reduce the set estimation problem to a function estimation problem.
This characterization precludes level sets with multiple connected components,
which are fundamental to many applications. This paper presents a fully
data-driven procedure that is adaptive to unknown regularity conditions and
achieves near minimax optimal Hausdorff error control for a class of density
level sets with very general shapes and multiple connected components.

本文针对基于密度函数的水平集的估计问题提出了一种全数据驱动的方法，并且该方法是自适应未知正则性条件的，可以实现多个连接组件和非常一般的形状的密度水平集的接近最小化最优 Hausdorff 误差控制。