论文标题
任务自适应功能变压器，具有语义富集，以进行几次分割
Task-Adaptive Feature Transformer with Semantic Enrichment 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，该模块可以放置在现有的分割网络的顶部，以执行很少的分割。该模块称为任务自适应功能变压器（TAFT），将特定于任务的高级特征转换为一组非常适合进行少数分段的任务不可能的特征。任务条件的特征转换允许在新颖类中有效利用语义信息，以生成紧密的分割面罩。我们还提出了一个语义富集（SE）模块，该模块利用像素的注意模块来实现高级特征，以及从辅助分割网络中进行所有培训类别的语义分割的辅助损失。 Pascal- $ 5^i $和可可 - $ 20^i $数据集的实验证实，添加的模块成功地扩展了现有分段者的能力，以产生竞争激烈的几杆分段性能。
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 that can be placed on top of existing segmentation networks for performing few-shot segmentation. This module, called the task-adaptive feature transformer (TAFT), linearly transforms task-specific high-level features to a set of task agnostic features well-suited to conducting few-shot segmentation. The task-conditioned feature transformation allows an effective utilization of the semantic information in novel classes to generate tight segmentation masks. We also propose a semantic enrichment (SE) module that utilizes a pixel-wise attention module for high-level feature and an auxiliary loss from an auxiliary segmentation network conducting the semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets confirm that the added modules successfully extend the capability of existing segmentators to yield highly competitive few-shot segmentation performances.
