An Empirical Study of Attention Networks for Semantic Segmentation
Hao Guo Hongbiao Si Guilin Jiang Wei Zhang Zhiyan Liu Xuanyi Zhu Xulong Zhang Yang Liu
Semantic segmentation is a vital problem in computer vision. Recently, a
common solution to semantic segmentation is the end-to-end convolution neural
network, which is much more accurate than traditional methods.Recently, the
decoders based on attention achieve state-of-the-art (SOTA) performance on
various datasets. But these networks always are compared with the mIoU of
previous SOTA networks to prove their superiority and ignore their
characteristics without considering the computation complexity and precision in
various categories, which is essential for engineering applications. Besides,
the methods to analyze the FLOPs and memory are not consistent between
different networks, which makes the comparison hard to be utilized. What's
more, various methods utilize attention in semantic segmentation, but the
conclusion of these methods is lacking. This paper first conducts experiments
to analyze their computation complexity and compare their performance. Then it
summarizes suitable scenes for these networks and concludes key points that
should be concerned when constructing an attention network. Last it points out
some future directions of the attention network.