Self-supervised learning is a promising paradigm in deep learning that
enables learning from unlabeled data by constructing pretext tasks that require
learning useful representations. In natural language processing, the dominant
pretext task has been masked language modeling (MLM), while in computer vision
there exists an equivalent called Masked Image Modeling (MIM). However, MIM is
challenging because it requires predicting semantic content in accurate
locations. E.g, given an incomplete picture of a dog, we can guess that there
is a tail, but we cannot determine its exact location. In this work, we propose
FlexPredict, a stochastic model that addresses this challenge by incorporating
location uncertainty into the model. Specifically, we condition the model on
stochastic masked token positions to guide the model toward learning features
that are more robust to location uncertainties. Our approach improves
downstream performance on a range of tasks, e.g, compared to MIM baselines,
FlexPredict boosts ImageNet linear probing by 1.6% with ViT-B and by 2.5% for
semi-supervised video segmentation using ViT-L.

提出了一种灵活的随机模型（FlexPredict），通过将位置不确定性纳入模型，提高下游性能。

在随机位置预测遮蔽标记以改进遮蔽图像建模

Predicting masked tokens in stochastic locations improves masked image  modeling

Single-Molecule Localization Microscopy (SMLM) has expanded our ability to
visualize subcellular structures but is limited in its temporal resolution.
Increasing emitter density will improve temporal resolution, but current
analysis algorithms struggle as emitter images significantly overlap. Here we
present a deep convolutional neural network called LUENN which utilizes a
unique architecture that rejects the isolated emitter assumption; it can
smoothly accommodate emitters that range from completely isolated to
co-located. This architecture, alongside an accurate estimator of location
uncertainty, extends the range of usable emitter densities by a factor of 6 to
over 31 emitters per micrometer-squared with reduced penalty to localization
precision and improved temporal resolution. Apart from providing uncertainty
estimation, the algorithm improves usability in laboratories by reducing
imaging times and easing requirements for successful experiments.

这篇研究论文介绍了一种新型的深度卷积神经网络 LUENN，可以有效提高单分子定位显微镜技术的亚细胞结构可视化的时间分辨率，通过准确的位置不确定性估计器，成功应用于密度高达 31 个 / 平方微米的多个发射器的图像定位。