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Weakly-supervised instance segmentation aims to detect and segment objectinstances precisely, given imagelevel labels only. Unlike previous methodswhich are composed of multiple offline stages, we propose Sequential LabelPropagation and Enhancement Networks (referred as Label-PEnet) thatprogressively transform image-level labels to pixel-wise labels in acoarse-to-fine manner. We design four cascaded modules including multi-labelclassification, object detection, instance refinement and instancesegmentation, which are implemented sequentially by sharing the same backbone.The cascaded pipeline is trained alternatively with a curriculum learningstrategy that generalizes labels from high-level images to low-level pixelsgradually with increasing accuracy. In addition, we design a proposalcalibration module to explore the ability of classification networks to findkey pixels that identify object parts, which serves as a post validationstrategy running in the inverse order. We evaluate the efficiency of ourLabel-PEnet in mining instance masks on standard benchmarks: PASCAL VOC 2007and 2012. Experimental results show that Label-PEnet outperforms thestate-of-the-art algorithms by a clear margin, and obtains comparableperformance even with the fully-supervised approaches.

Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation