Weakly supervised learning based on scribble annotations in target extraction
of remote sensing images has drawn much interest due to scribbles' flexibility
in denoting winding objects and low cost of manually labeling. However,
scribbles are too sparse to identify object structure and detailed information,
bringing great challenges in target localization and boundary description. To
alleviate these problems, in this paper, we construct two inner
structure-constraints, a deformation consistency loss and a trainable active
contour loss, together with a scribble-constraint to supervise the optimization
of the encoder-decoder network without introducing any auxiliary module or
extra operation based on prior cues. Comprehensive experiments demonstrate our
method's superiority over five state-of-the-art algorithms in this field.
Source code is available at this https URL

本研究提出一种基于线条注释的弱监督学习方法，在不引入辅助模块或依赖预先消息的情况下，通过两个内部结构约束和一个 scribble 约束来完善编码器 - 解码器网络的优化，从而实现遥感图像目标提取的定位和边界描述，实验证明其在此领域优于其他五种最先进的算法。

基于内部结构约束的涂鸦辅助遥感图像目标提取方法

Scribble-Supervised Target Extraction Method Based on Inner  Structure-Constraint for Remote Sensing Images

Fine-grained sentiment analysis attempts to extract sentiment holders,
targets and polar expressions and resolve the relationship between them, but
progress has been hampered by the difficulty of annotation. Targeted sentiment
analysis, on the other hand, is a more narrow task, focusing on extracting
sentiment targets and classifying their polarity.In this paper, we explore
whether incorporating holder and expression information can improve target
extraction and classification and perform experiments on eight English
datasets. We conclude that jointly predicting target and polarity BIO labels
improves target extraction, and that augmenting the input text with gold
expressions generally improves targeted polarity classification. This
highlights the potential importance of annotating expressions for fine-grained
sentiment datasets. At the same time, our results show that performance of
current models for predicting polar expressions is poor, hampering the benefit
of this information in practice.

本文探索了加入情感持有者和情感表达信息是否能够改善目标提取和分类，并在 8 个英文数据集上进行了实验证明，预测目标和情感 BIO 标签可以提高目标提取，将输入文本增加黄金表达通常可以改善有针对性的极性分类。同时，我们的结果表明，目前模型预测极性表达的性能差，从而阻碍了实践中这些信息的利用。