  We address the problem of weakly-supervised semantic segmentation (WSSS)
using bounding box annotations. Although object bounding boxes are good
indicators to segment corresponding objects, they do not specify object
boundaries, making it hard to train convolutional neural networks (CNNs) for
semantic segmentation. We find that background regions are perceptually
consistent in part within an image, and this can be leveraged to discriminate
foreground and background regions inside object bounding boxes. To implement
this idea, we propose a novel pooling method, dubbed background-aware pooling
(BAP), that focuses more on aggregating foreground features inside the bounding
boxes using attention maps. This allows to extract high-quality pseudo
segmentation labels to train CNNs for semantic segmentation, but the labels
still contain noise especially at object boundaries. To address this problem,
we also introduce a noise-aware loss (NAL) that makes the networks less
susceptible to incorrect labels. Experimental results demonstrate that learning
with our pseudo labels already outperforms state-of-the-art weakly- and
semi-supervised methods on the PASCAL VOC 2012 dataset, and the NAL further
boosts the performance.
