Recent non-local self-attention methods have proven to be effective in
capturing long-range dependencies for semantic segmentation. These methods
usually form a similarity map of RC*C (by compressing spatial dimensions) or
RHW*HW (by compressing channels) to describe the feature relations along either
channel or spatial dimensions, where C is the number of channels, H and W are
the spatial dimensions of the input feature map. However, such practices tend
to condense feature dependencies along the other dimensions,hence causing
attention missing, which might lead to inferior results for small/thin
categories or inconsistent segmentation inside large objects. To address this
problem, we propose anew approach, namely Fully Attentional Network (FLANet),to
encode both spatial and channel attentions in a single similarity map while
maintaining high computational efficiency. Specifically, for each channel map,
our FLANet can harvest feature responses from all other channel maps, and the
associated spatial positions as well, through a novel fully attentional module.
Our new method has achieved state-of-the-art performance on three challenging
semantic segmentation datasets,i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes
test set,the ADE20K validation set, and the PASCAL VOC test set,respectively.

该研究提出了 FLANet 方法，可以通过单个相似度图来同时编码空间和通道注意力，并在三个具有挑战性的语义分割数据集上最先进的性能达到 83.6％，46.99％和 88.5％。