  Semantic segmentation for lightweight object parsing is a very challenging
task, because both accuracy and efficiency (e.g., execution speed, memory
footprint or computational complexity) should all be taken into account.
However, most previous works pay too much attention to one-sided perspective,
either accuracy or speed, and ignore others, which poses a great limitation to
actual demands of intelligent devices. To tackle this dilemma, we propose a
novel lightweight architecture named Context-Integrated and Feature-Refined
Network (CIFReNet). The core components of CIFReNet are the Long-skip
Refinement Module (LRM) and the Multi-scale Context Integration Module (MCIM).
The LRM is designed to ease the propagation of spatial information between
low-level and high-level stages. Furthermore, channel attention mechanism is
introduced into the process of long-skip learning to boost the quality of
low-level feature refinement. Meanwhile, the MCIM consists of three cascaded
Dense Semantic Pyramid (DSP) blocks with image-level features, which is
presented to encode multiple context information and enlarge the field of view.
Specifically, the proposed DSP block exploits a dense feature sampling strategy
to enhance the information representations without significantly increasing the
computation cost. Comprehensive experiments are conducted on three benchmark
datasets for object parsing including Cityscapes, CamVid, and Helen. As
indicated, the proposed method reaches a better trade-off between accuracy and
efficiency compared with the other state-of-the-art methods.
