论文标题
综合少量学习用于分类和细分
Integrative Few-Shot Learning for Classification and Segmentation
论文作者
论文摘要
我们介绍了几个射击分类和分割（FS-CS）的综合任务，该任务旨在在给出一些示例的目标类时在查询映像中对目标对象进行分类和细分目标对象。这项任务结合了两个常规的少数学习问题，几乎没有射击分类和细分。 FS-CS将它们概括为具有任意图像对的更现实的情节，在查询中每个目标类可能都可能存在或不存在。为了解决该任务，我们建议FS-CS的综合学习框架（IFSL）框架，该框架训练学习者，以构建班级前景图，以进行多标签分类和像素细分。我们还开发了一个有效的IFSL模型，细心的挤压网络（ASNET），该模型利用了深厚的语义相关性和全球自我注意力来产生可靠的前景图。在实验中，提出的方法在FS-CS任务上显示出有希望的表现，并且还以标准的几杆分割基准实现了最新的状态。
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This task combines two conventional few-shot learning problems, few-shot classification and segmentation. FS-CS generalizes them to more realistic episodes with arbitrary image pairs, where each target class may or may not be present in the query. To address the task, we propose the integrative few-shot learning (iFSL) framework for FS-CS, which trains a learner to construct class-wise foreground maps for multi-label classification and pixel-wise segmentation. We also develop an effective iFSL model, attentive squeeze network (ASNet), that leverages deep semantic correlation and global self-attention to produce reliable foreground maps. In experiments, the proposed method shows promising performance on the FS-CS task and also achieves the state of the art on standard few-shot segmentation benchmarks.
