论文标题
用于几次分段的班级感知和类不足的对齐方式的联合框架
A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation
论文作者
论文摘要
很少有射击分段（FSS）旨在仅给出几个带注释的支持图像的看不见类的对象。大多数现有方法只是将查询特征带有独立支持原型，并通过将混合功能馈送到解码器来分割查询图像。尽管已经取得了重大改进，但由于阶级变体和背景混乱，现有方法仍然是阶级偏见。在本文中，我们提出了一个联合框架，该框架结合了更有价值的班级感知和类不足的一致性指导，以促进细分。具体而言，我们设计了一个混合对齐模块，该模块从相应的支持功能中建立了多尺度的查询支持对应关系，以挖掘每个查询图像的最相关的类感知信息。此外，我们还探索利用基类知识来生成类不足的先验掩码，该掩码通过突出所有对象区域，尤其是看不见的类别来区分真实背景和前景。通过共同汇总班级感知和类不足的比对指导，可以在查询图像上获得更好的分割性能。 Pascal- $ 5^i $和可可$ 20^i $数据集的大量实验表明，我们提出的拟议的联合框架的性能更好，尤其是在1次设置上。
Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face class biases due to class variants and background confusion. In this paper, we propose a joint framework that combines more valuable class-aware and class-agnostic alignment guidance to facilitate the segmentation. Specifically, we design a hybrid alignment module which establishes multi-scale query-support correspondences to mine the most relevant class-aware information for each query image from the corresponding support features. In addition, we explore utilizing base-classes knowledge to generate class-agnostic prior mask which makes a distinction between real background and foreground by highlighting all object regions, especially those of unseen classes. By jointly aggregating class-aware and class-agnostic alignment guidance, better segmentation performances are obtained on query images. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that our proposed joint framework performs better, especially on the 1-shot setting.
