Automatic segmentation of fluid in OCT (Optical Coherence Tomography) images
is beneficial for ophthalmologists to make an accurate diagnosis. Currently,
data-driven convolutional neural networks (CNNs) have achieved great success in
OCT fluid segmentation. However, obtaining pixel-level masks of OCT images is
time-consuming and requires expertise. The popular weakly-supervised strategy
is to generate noisy pseudo-labels from weak annotations, but the noise
information introduced may mislead the model training. To address this issue,
(i) we propose a superpixel-guided method for generating noisy labels from weak
point annotations, called Point to Noisy by Superpixel (PNS), which limits the
network from over-fitting noise by assigning low confidence to suspiciously
noisy label pixels, and (ii) we propose a Two-Stage Mean-Teacher-assisted
Confident Learning (2SMTCL) method based on MTCL for multi-category OCT fluid
segmentation, which alleviates the uncertainty and computing power consumption
introduced by the real-time characterization noise of MTCL. For evaluation, we
have constructed a 2D OCT fluid segmentation dataset. Compared with other
state-of-art label-denoising methods, comprehensive experimental results
demonstrate that the proposed method can achieve excellent performance in OCT
fluid segmentation as well as label denoising. Our study provides an efficient,
accurate, and practical solution for fluid segmentation of OCT images, which is
expected to have a positive impact on the diagnosis and treatment of patients
in the field of ophthalmology.

本文提出了一种基于超像素指导的用于从弱标注中生成噪声标签的方法（PNS），以及一种基于 MTCL 的 Two-Stage Mean-Teacher-assisted Confident Learning（2SMTCL）方法，用于多类别 OCT 流体分割。实验结果表明，该方法在 OCT 流体分割以及标签去噪方面性能优秀，为眼科领域患者的诊断和治疗提供了高效、准确和实用的解决方案。

使用极端点注释生成的噪声标签进行 OCT 液体分割的学习

Learning from Noisy Labels Generated by Extremely Point Annotations for  OCT Fluid Segmentation

Document-level relation extraction (DocRE) aims to infer complex semantic
relations among entities in a document. Distant supervision (DS) is able to
generate massive auto-labeled data, which can improve DocRE performance. Recent
works leverage pseudo labels generated by the pre-denoising model to reduce
noise in DS data. However, unreliable pseudo labels bring new noise, e.g.,
adding false pseudo labels and losing correct DS labels. Therefore, how to
select effective pseudo labels to denoise DS data is still a challenge in
document-level distant relation extraction. To tackle this issue, we introduce
uncertainty estimation technology to determine whether pseudo labels can be
trusted. In this work, we propose a Document-level distant Relation Extraction
framework with Uncertainty Guided label denoising, UGDRE. Specifically, we
propose a novel instance-level uncertainty estimation method, which measures
the reliability of the pseudo labels with overlapping relations. By further
considering the long-tail problem, we design dynamic uncertainty thresholds for
different types of relations to filter high-uncertainty pseudo labels. We
conduct experiments on two public datasets. Our framework outperforms strong
baselines by 1.91 F1 and 2.28 Ign F1 on the RE-DocRED dataset.

本文提出了一种使用不确定性估计技术的文档层次远程关系提取框架 UDGDE，它通过新颖的实例级不确定性估计方法测量重叠关系的伪标签的可靠性，并针对长尾问题设计动态的不确定性阈值以过滤高不确定性的伪标签，实验证明该框架在两个公共数据集上性能优于基线算法。

基于不确定性的文档级远程关系抽取标签去噪

Uncertainty Guided Label Denoising for Document-level Distant Relation  Extraction

Finding dense semantic correspondence is a fundamental problem in computer
vision, which remains challenging in complex scenes due to background clutter,
extreme intra-class variation, and a severe lack of ground truth. In this
paper, we aim to address the challenge of label sparsity in semantic
correspondence by enriching supervision signals from sparse keypoint
annotations. To this end, we first propose a teacher-student learning paradigm
for generating dense pseudo-labels and then develop two novel strategies for
denoising pseudo-labels. In particular, we use spatial priors around the sparse
annotations to suppress the noisy pseudo-labels. In addition, we introduce a
loss-driven dynamic label selection strategy for label denoising. We
instantiate our paradigm with two variants of learning strategies: a single
offline teacher setting, and mutual online teachers setting. Our approach
achieves notable improvements on three challenging benchmarks for semantic
correspondence and establishes the new state-of-the-art. Project page:
this https URL

本文针对语义对应中标签稀疏性的挑战，提出了一种基于稀疏关键点注释生成密集伪标签的教师 - 学生学习范式，并开发了两种噪声伪标签去噪策略，使用空间先验和损失驱动的动态标签选择策略。实验结果表明该方法在语义对应的三个基准测试中均取得了显著进展并建立了新的最新成果。