# Context-Integrated and Feature-Refined Network for Lightweight Object Parsing

## TL;DR

This paper introduces CIFReNet, a lightweight neural network architecture for object parsing that balances accuracy and efficiency through innovative modules for spatial information propagation and multi-scale context encoding.

## Contribution

The paper proposes CIFReNet with novel modules LRM and MCIM, including DSP blocks with dense sampling, to improve lightweight object parsing performance.

## Key findings

- Achieves better accuracy-efficiency trade-off than state-of-the-art methods.
- Demonstrates strong performance on Cityscapes, CamVid, and Helen datasets.
- Effective in real-world applications requiring fast and accurate semantic segmentation.

## Abstract

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.