In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods. To solve such a challenging problem, we leverage several low-level cues (such as saliency, edges, etc.) to help generate a proxy ground truth. Due to the diversity of web-crawled images, we anticipate a large amount of 'label noise' in which other objects might be present. We design an online noise filtering scheme which is able to deal with this label noise, especially in cluttered images. We use this filtering strategy as an auxiliary module to help assist the segmentation network in learning cleaner proxy annotations. Extensive experiments on the popular PASCAL VOC 2012 semantic segmentation benchmark show surprising good results in both our WebSeg (mIoU = 57.0%) and weakly supervised (mIoU = 63.3%) settings.

本文提出一种能够无需依赖显式用户注释的Flickr图像，通过利用一些低级线索（如显著性、边缘等）生成代理标签，并使用在线标签噪声过滤辅助模块来帮助协助分割网络学习更干净的代理注释进行语义分割。在PASCAL VOC 2012语义分割基准测试中，该算法表现出色，WebSeg(mIoU = 57.0%)和弱监督(mIoU = 63.3%)的设置。

WebSeg：从网络搜索中学习语义分割