Current weakly-supervised incremental learning for semantic segmentation (WILSS) approaches only consider replacing pixel-level annotations with image-level labels, while the training images are still from well-designed datasets. In this work, we argue that widely available web images can also be considered for the learning of new classes. To achieve this, firstly we introduce a strategy to select web images which are similar to previously seen examples in the latent space using a Fourier-based domain discriminator. Then, an effective caption-driven reharsal strategy is proposed to preserve previously learnt classes. To our knowledge, this is the first work to rely solely on web images for both the learning of new concepts and the preservation of the already learned ones in WILSS. Experimental results show that the proposed approach can reach state-of-the-art performances without using manually selected and annotated data in the incremental steps.

当前的弱监督增量学习用于语义分割（WILSS）方法仅考虑使用图像级别标签替换像素级注释，而训练图像仍来自精心设计的数据集。在这项工作中，我们认为普遍可用的网络图像也可以用于学习新的类别。为了实现这一目标，首先我们引入了一种策略，使用基于傅里叶变换的域判别器在潜在空间中选择与先前示例相似的网络图像。然后，提出了一种有效的基于标题的排练策略，用于保留先前学习到的类别。据我们所知，这是第一项仅依赖网络图像来学习新概念和保留已学习概念的WILSS工作。实验结果表明，该方法在增量步骤中无需使用手动选择和注释的数据即可达到最先进的性能。

从互联网学习：语言驱动弱监督增量学习用于语义分割