HM: Hybrid Masking for Few-Shot Segmentation-猫眼课题宝

DOI：

10.1007/978-3-031-20044-1_29

发表时间：

2022-03

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作者：

Seonghyeon Moon;Samuel S. Sohn;Honglu Zhou;Sejong Yoon;V. Pavlovic;Muhammad Haris Khan;M. Kapadia

我们研究了少镜头语义分割，其目的是从查询图像中分割出目标对象时，提供了一些注释的支持图像的目标类。最近的几种方法诉诸于特征掩蔽（FM）技术，以丢弃不相关的特征激活，最终有利于可靠的预测分割掩模。FM的一个基本限制是无法保留影响分割掩模精度的细粒度空间细节，特别是对于小目标对象。在本文中，我们开发了一种简单，有效，高效的方法来增强特征掩蔽（FM）。我们称之为混合掩蔽（HM）的增强FM。具体来说，我们通过调查和利用一个互补的基本输入掩蔽方法来补偿FM技术中细粒度空间细节的损失。实验已经进行了三个公开的基准与强大的少拍分割（FSS）基线。我们通过不同基准的可见利润率，实证显示了对当前最先进方法的性能改善。我们的代码和经过训练的模型可在以下网址获得：https://github.com/moonsh/HM-Hybrid-Masking

We study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. Several recent methods resort to a feature masking (FM) technique to discard irrelevant feature activations which eventually facilitates the reliable prediction of segmentation mask. A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects. In this paper, we develop a simple, effective, and efficient approach to enhance feature masking (FM). We dub the enhanced FM as hybrid masking (HM). Specifically, we compensate for the loss of fine-grained spatial details in FM technique by investigating and leveraging a complementary basic input masking method. Experiments have been conducted on three publicly available benchmarks with strong few-shot segmentation (FSS) baselines. We empirically show improved performance against the current state-of-the-art methods by visible margins across different benchmarks. Our code and trained models are available at: https://github.com/moonsh/HM-Hybrid-Masking

HM: Hybrid Masking for Few-Shot Segmentation

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