Resolution-Aware Design of Atrous Rates for Semantic Segmentation
  Networks
Bum Jun Kim Hyeyeon Choi Hyeonah Jang Sang Woo Kim
DeepLab is a widely used deep neural network for semantic segmentation, whose
success is attributed to its parallel architecture called atrous spatial
pyramid pooling (ASPP). ASPP uses multiple atrous convolutions with different
atrous rates to extract both local and global information. However, fixed
values of atrous rates are used for the ASPP module, which restricts the size
of its field of view. In principle, atrous rate should be a hyperparameter to
change the field of view size according to the target task or dataset. However,
the manipulation of atrous rate is not governed by any guidelines. This study
proposes practical guidelines for obtaining an optimal atrous rate. First, an
effective receptive field for semantic segmentation is introduced to analyze
the inner behavior of segmentation networks. We observed that the use of ASPP
module yielded a specific pattern in the effective receptive field, which was
traced to reveal the module's underlying mechanism. Accordingly, we derive
practical guidelines for obtaining the optimal atrous rate, which should be
controlled based on the size of input image. Compared to other values, using
the optimal atrous rate consistently improved the segmentation results across
multiple datasets, including the STARE, CHASE_DB1, HRF, Cityscapes, and iSAID
datasets.