  We propose a novel deep layer cascade (LC) method to improve the accuracy and
speed of semantic segmentation. Unlike the conventional model cascade (MC) that
is composed of multiple independent models, LC treats a single deep model as a
cascade of several sub-models. Earlier sub-models are trained to handle easy
and confident regions, and they progressively feed-forward harder regions to
the next sub-model for processing. Convolutions are only calculated on these
regions to reduce computations. The proposed method possesses several
advantages. First, LC classifies most of the easy regions in the shallow stage
and makes deeper stage focuses on a few hard regions. Such an adaptive and
'difficulty-aware' learning improves segmentation performance. Second, LC
accelerates both training and testing of deep network thanks to early decisions
in the shallow stage. Third, in comparison to MC, LC is an end-to-end trainable
framework, allowing joint learning of all sub-models. We evaluate our method on
PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and
fast speed.
