As one of the most fundamental tasks in computer vision, semantic segmentation assigns per pixel prediction of object categories. Training a robust model for semantic segmentation is challenging since pixel-level annotations are expensive to obtain. To alleviate the burden of annotations, the authors propose a weakly-supervised framework for zero-shot semantic segmentation, which can segment images having target classes without any pixel-level labelled instances. Under the assumption that the accessibility to image-level annotations of target classes does not violate the principle of zero pixel-level label in zero-shot semantic segmentation, we utilised image-level annotations to improve the proposed model's ability to extract pixel-level features. Furthermore, unlike existing zero-shot semantic segmentation methods, which use semantic embeddings as class embeddings to transfer knowledge from source classes to target classes, we use image-level features as their class embeddings to transfer knowledge since the distribution of pixel-level features is more similar to the distribution of image-level features rather than the distribution of semantic embeddings. Experimental results on the PASCAL-VOC data set under different data splits demonstrate that the proposed model achieves promising results.

中文翻译：

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用于零样本语义分割的弱监督分类模型

作为计算机视觉中最基本的任务之一，语义分割分配对象类别的每个像素预测。训练用于语义分割的鲁棒模型具有挑战性，因为获得像素级注释的成本很高。为了减轻注释的负担，作者提出了一种用于零镜头语义分割的弱监督框架，该框架可以在没有任何像素级标记实例的情况下分割具有目标类别的图像。在目标类的图像级注释的可访问性不违反零镜头语义分割中零像素级标签原则的假设下，我们利用图像级注释来提高所提出的模型提取像素级特征的能力. 此外，与现有的零镜头语义分割方法不同，使用语义嵌入作为类嵌入将知识从源类转移到目标类，我们使用图像级特征作为它们的类嵌入来转移知识，因为像素级特征的分布更类似于图像级特征的分布而不是而不是语义嵌入的分布。在不同数据拆分下的 PASCAL-VOC 数据集上的实验结果表明，所提出的模型取得了有希望的结果。