Misc,
CCNet: Criss-Cross Attention for Semantic Segmentation
(Nov 28, 2018)
Abstract
Long-range dependencies can capture useful contextual information to benefit
visual understanding problems. In this work, we propose a Criss-Cross Network
(CCNet) for obtaining such important information through a more effective and
efficient way. Concretely, for each pixel, our CCNet can harvest the contextual
information of its surrounding pixels on the criss-cross path through a novel
criss-cross attention module. By taking a further recurrent operation, each
pixel can finally capture the long-range dependencies from all pixels. Overall,
our CCNet is with the following merits: 1) GPU memory friendly. Compared with
the non-local block, the recurrent criss-cross attention module requires
\$11\times\$ less GPU memory usage. 2) High computational efficiency. The
recurrent criss-cross attention significantly reduces FLOPs by about 85\% of
the non-local block in computing long-range dependencies. 3) The
state-of-the-art performance. We conduct extensive experiments on popular
semantic segmentation benchmarks including Cityscapes, ADE20K, and instance
segmentation benchmark COCO. In particular, our CCNet achieves the mIoU score
of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set,
respectively, which are the new state-of-the-art results. We make the code
publicly available at <a href="https://github.com/speedinghzl/CCNet">this https URL</a> .
