Existing shadow detection datasets often contain missing or mislabeled
shadows, which can hinder the performance of deep learning models trained
directly on such data. To address this issue, we propose SILT, the Shadow-aware
Iterative Label Tuning framework, which explicitly considers noise in shadow
labels and trains the deep model in a self-training manner. Specifically, we
incorporate strong data augmentations with shadow counterfeiting to help the
network better recognize non-shadow regions and alleviate overfitting. We also
devise a simple yet effective label tuning strategy with global-local fusion
and shadow-aware filtering to encourage the network to make significant
refinements on the noisy labels. We evaluate the performance of SILT by
relabeling the test set of the SBU dataset and conducting various experiments.
Our results show that even a simple U-Net trained with SILT can outperform all
state-of-the-art methods by a large margin. When trained on SBU / UCF / ISTD,
our network can successfully reduce the Balanced Error Rate by 25.2% / 36.9% /
21.3% over the best state-of-the-art method.

提出了基于 SILT 的阴影感知迭代标签调整框架，通过将强数据增强与阴影伪造相结合，采用全局 - 局部融合和阴影感知过滤的简单且有效的标签调整策略，以从噪声标签中让网络进行重要的改进，实验证明使用 SILT 训练的简单 U-Net 模型在阴影检测上优于所有最先进的方法，当训练于 SBU / UCF / ISTD 上时，我们的网络能够成功地将平衡错误率降低为最佳方法的 25.2％/ 36.9％/ 21.3％。

SILT: 基于阴影感知的迭代标签调整用于学习从噪声标签中检测阴影

SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows  from Noisy Labels

We study the problem of building text classifiers with little or no training
data, commonly known as zero and few-shot text classification. In recent years,
an approach based on neural textual entailment models has been found to give
strong results on a diverse range of tasks. In this work, we show that with
proper pre-training, Siamese Networks that embed texts and labels offer a
competitive alternative. These models allow for a large reduction in inference
cost: constant in the number of labels rather than linear. Furthermore, we
introduce label tuning, a simple and computationally efficient approach that
allows to adapt the models in a few-shot setup by only changing the label
embeddings. While giving lower performance than model fine-tuning, this
approach has the architectural advantage that a single encoder can be shared by
many different tasks.

本文介绍了使用神经网络模型解决文本分类中少样本或无标注样本问题的方法，通过预训练 Siamese Networks 嵌入文本和标签的方式得到了与基于文本蕴含的方法相当的性能，同时增加了标签调整方法，可在少样本的情况下仅通过改变标签嵌入来完成模型调整。