MCFNet: Multi-Attentional Class Feature Augmentation Network for Real-Time Scene Parsing

Author:

Wang Xizhong1ORCID,Liu Rui1ORCID,Yang Xin2ORCID,Zhang Qiang2ORCID,Zhou Dongsheng1ORCID

Affiliation:

1. National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China

2. School of Computer Science, Dalian University of Technology, Dalian, China

Abstract

For real-time scene parsing tasks, capturing multi-scale semantic features and performing effective feature fusion is crucial. However, many existing solutions ignore stripe-shaped things like poles, traffic lights and are so computationally expensive that cannot meet the high real-time requirements. This article presents a novel model, the Multi-Attention Class Feature Augmentation Network (MCFNet) to address this challenge. MCFNet is designed to capture long-range dependencies across different scales with low computational cost and to perform a weighted fusion of feature maps. It features the BAM (Strip Matrix Based Attention Module) for extracting strip objects in images. The BAM module replaces the conventional self-attention method using square matrices with strip matrices, which allows it to focus more on strip objects while reducing computation. Additionally, MCFNet has a parallel branch that focuses on global information based on self-attention to avoid wasting computation. The two branches are merged to enhance the performance of traditional self-attention modules. Experimental results on two mainstream datasets demonstrate the effectiveness of MCFNet. On the Camvid and Cityscapes test sets, MCFNet achieved 207.5 FPS/73.5% mIoU and 136.1 FPS/71.63% mIoU, respectively. The experiments show that MCFNet outperforms other models on the Camvid dataset and can significantly improve the performance of real-time scene parsing tasks.

Funder

Key Project of NSFC

Program for Innovative Research Team in University of Liaoning Province

Support Plan for Key Field Innovation Team of Dalian

Support Plan for Leading Innovation Team of Dalian University

Science and Technology Innovation Fund of Dalian

111 Project

Publisher

Association for Computing Machinery (ACM)

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