论文标题
少量分段的任务自适应功能变压器
Task-Adaptive Feature Transformer for Few-Shot Segmentation
论文作者
论文摘要
很少有学习的学习允许机器仅使用几个标记的样本对新颖类进行分类。最近，针对低样本数据的语义细分的几乎没有分割也引起了人们的极大兴趣。在本文中，我们提出了一个可学习的模块，用于几个射击分割，即任务自动特征变压器（TAFT）。 TAFT线性将特定于任务的高级功能转换为一组非常适合分割作业的任务无关功能。使用此任务条件的特征转换，该模型被证明可以有效利用新颖类中的语义信息来生成紧密的分割掩码。所提出的TAFT模块可以轻松地插入现有的语义分割算法中，以实现仅使用几个添加参数的几个弹片分割功能。我们将TAFT与DeepLab V3+（一种众所周知的分割结构）相结合； Pascal- $ 5^i $数据集的实验证实，这种组合成功地为细分算法增添了很少的学习能力，在某些关键代表性的情况下实现了最先进的少数细分性能。
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module for few-shot segmentation, the task-adaptive feature transformer (TAFT). TAFT linearly transforms task-specific high-level features to a set of task-agnostic features well-suited to the segmentation job. Using this task-conditioned feature transformation, the model is shown to effectively utilize the semantic information in novel classes to generate tight segmentation masks. The proposed TAFT module can be easily plugged into existing semantic segmentation algorithms to achieve few-shot segmentation capability with only a few added parameters. We combine TAFT with Deeplab V3+, a well-known segmentation architecture; experiments on the PASCAL-$5^i$ dataset confirm that this combination successfully adds few-shot learning capability to the segmentation algorithm, achieving the state-of-the-art few-shot segmentation performance in some key representative cases.
