  Objects at different spatial positions in an image exhibit different scales.
Adaptive receptive fields are expected to capture suitable ranges of context
for accurate pixel level semantic prediction. Recently, atrous convolution with
different dilation rates has been used to generate features of multi-scales
through several branches which are then fused for prediction. However, there is
a lack of explicit interaction among the branches of different scales to
adaptively make full use of the contexts. In this paper, we propose a
Content-Adaptive Scale Interaction Network (CASINet) to exploit the multi-scale
features for scene parsing. We build CASINet based on the classic Atrous
Spatial Pyramid Pooling (ASPP) module, followed by a proposed contextual scale
interaction (CSI) module, and a scale adaptation (SA) module. Specifically, in
the CSI module, for each spatial position of some scale, instead of being
limited by a fixed set of convolutional filters that are shared across
different spatial positions for feature learning, we promote the adaptivity of
the convolutional filters to spatial positions. We achieve this by the context
interaction among the features of different scales. The SA module explicitly
and softly selects the suitable scale for each spatial position and each
channel. Ablation studies demonstrate the effectiveness of the proposed
modules. We achieve state-of-the-art performance on three scene parsing
benchmarks Cityscapes, ADE20K and LIP.
