  In this paper, we address the scene segmentation task by capturing rich
contextual dependencies based on the selfattention mechanism. Unlike previous
works that capture contexts by multi-scale features fusion, we propose a Dual
Attention Networks (DANet) to adaptively integrate local features with their
global dependencies. Specifically, we append two types of attention modules on
top of traditional dilated FCN, which model the semantic interdependencies in
spatial and channel dimensions respectively. The position attention module
selectively aggregates the features at each position by a weighted sum of the
features at all positions. Similar features would be related to each other
regardless of their distances. Meanwhile, the channel attention module
selectively emphasizes interdependent channel maps by integrating associated
features among all channel maps. We sum the outputs of the two attention
modules to further improve feature representation which contributes to more
precise segmentation results. We achieve new state-of-the-art segmentation
performance on three challenging scene segmentation datasets, i.e., Cityscapes,
PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5%
on Cityscapes test set is achieved without using coarse data. We make the code
and trained model publicly available at https://github.com/junfu1115/DANet
