  Noisy labels, inevitably existing in pseudo segmentation labels generated
from weak object-level annotations, severely hampers model optimization for
semantic segmentation. Previous works often rely on massive hand-crafted losses
and carefully-tuned hyper-parameters to resist noise, suffering poor
generalization capability and high model complexity. Inspired by recent
advances in meta learning, we argue that rather than struggling to tolerate
noise hidden behind clean labels passively, a more feasible solution would be
to find out the noisy regions actively, so as to simply ignore them during
model optimization. With this in mind, this work presents a novel meta learning
based semantic segmentation method, MetaSeg, that comprises a primary
content-aware meta-net (CAM-Net) to sever as a noise indicator for an arbitrary
segmentation model counterpart. Specifically, CAM-Net learns to generate
pixel-wise weights to suppress noisy regions with incorrect pseudo labels while
highlighting clean ones by exploiting hybrid strengthened features from image
content, providing straightforward and reliable guidance for optimizing the
segmentation model. Moreover, to break the barrier of time-consuming training
when applying meta learning to common large segmentation models, we further
present a new decoupled training strategy that optimizes different model layers
in a divide-and-conquer manner. Extensive experiments on object, medical,
remote sensing and human segmentation shows that our method achieves superior
performance, approaching that of fully supervised settings, which paves a new
promising way for omni-supervised semantic segmentation.
