Deep convolutional neural networks (DCNNs) trained on a large number of
images with strong pixel-level annotations have recently significantly pushed
the state-of-art in semantic image segmentation. We study the more challenging
problem of learning DCNNs for semantic image segmentation from either (1)
weakly annotated training data such as bounding boxes or image-level labels or
(2) a combination of few strongly labeled and many weakly labeled images,
sourced from one or multiple datasets. We develop Expectation-Maximization (EM)
methods for semantic image segmentation model training under these weakly
supervised and semi-supervised settings. Extensive experimental evaluation
shows that the proposed techniques can learn models delivering competitive
results on the challenging PASCAL VOC 2012 image segmentation benchmark, while
requiring significantly less annotation effort. We share source code
implementing the proposed system at
this https URL

作者提出一种针对弱监督和半监督学习条件下的语义图像分割模型训练的 Expectation-Maximization 方法，实验表明所提技术具有竞争性的成果，同时需求更少的标注工作量。