  Weakly supervised semantic segmentation (WSSS) using only image-level labels
can greatly reduce the annotation cost and therefore has attracted considerable
research interest. However, its performance is still inferior to the fully
supervised counterparts. To mitigate the performance gap, we propose a saliency
guided self-attention network (SGAN) to address the WSSS problem. The
introduced self-attention mechanism is able to capture rich and extensive
contextual information but may mis-spread attentions to unexpected regions. In
order to enable this mechanism to work effectively under weak supervision, we
integrate class-agnostic saliency priors into the self-attention mechanism and
utilize class-specific attention cues as an additional supervision for SGAN.
Our SGAN is able to produce dense and accurate localization cues so that the
segmentation performance is boosted. Moreover, by simply replacing the
additional supervisions with partially labeled ground-truth, SGAN works
effectively for semi-supervised semantic segmentation as well. Experiments on
the PASCAL VOC 2012 and COCO datasets show that our approach outperforms all
other state-of-the-art methods in both weakly and semi-supervised settings.
