Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations

• URL: http://arxiv.org/abs/2007.01496v2
• Date: Fri, 18 Jun 2021 17:55:54 GMT
• Title: Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations
• Authors: Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu
• Abstract summary: Recent progress in fewshot semantic segmentation tackles the issue by only a few pixel-level annotated examples. Our key idea is to learn a better prototype representation of the class by fusing the knowledge from the image-level labeled data. We propose a new framework, called PAIA, to learn the class prototype representation in a metric space by integrating image-level annotations.
• Score: 23.02986307143718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-networkbased methods typically suffer from a shortage of large amounts of pixel-level annotations. Recent progress in fewshot semantic segmentation tackles the issue by only a few pixel-level annotated examples. However, these few-shot approaches cannot easily be applied to multi-way or weak annotation settings. In this paper, we advance the few-shot segmentation paradigm towards a scenario where image-level annotations are available to help the training process of a few pixel-level annotations. Our key idea is to learn a better prototype representation of the class by fusing the knowledge from the image-level labeled data. Specifically, we propose a new framework, called PAIA, to learn the class prototype representation in a metric space by integrating image-level annotations. Furthermore, by considering the uncertainty of pseudo-masks, a distilled soft masked average pooling strategy is designed to handle distractions in image-level annotations. Extensive empirical results on two datasets show superior performance of PAIA.

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