  Real-time semantic segmentation has received considerable attention due to
growing demands in many practical applications, such as autonomous vehicles,
robotics, etc. Existing real-time segmentation approaches often utilize feature
fusion to improve segmentation accuracy. However, they fail to fully consider
the feature information at different resolutions and the receptive fields of
the networks are relatively limited, thereby compromising the performance. To
tackle this problem, we propose a light Cascaded Selective Resolution Network
(CSRNet) to improve the performance of real-time segmentation through multiple
context information embedding and enhanced feature aggregation. The proposed
network builds a three-stage segmentation system, which integrates feature
information from low resolution to high resolution and achieves feature
refinement progressively. CSRNet contains two critical modules: the Shorted
Pyramid Fusion Module (SPFM) and the Selective Resolution Module (SRM). The
SPFM is a computationally efficient module to incorporate the global context
information and significantly enlarge the receptive field at each stage. The
SRM is designed to fuse multi-resolution feature maps with various receptive
fields, which assigns soft channel attentions across the feature maps and helps
to remedy the problem caused by multi-scale objects. Comprehensive experiments
on two well-known datasets demonstrate that the proposed CSRNet effectively
improves the performance for real-time segmentation.
