首页|Hebei University of Engineering Researcher Highlights Research in Computational Intelligence (Research on Efficient Asymmetric Attention Module for Real-Time Se mantic Segmentation Networks in Urban Scenes)
Hebei University of Engineering Researcher Highlights Research in Computational Intelligence (Research on Efficient Asymmetric Attention Module for Real-Time Se mantic Segmentation Networks in Urban Scenes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on co mputational intelligence. According to news originating from Hebei, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Currently, numerous high-precision models have been proposed for semantic segmentation, but the mode l parameters are large and the segmentation speed is slow.” Our news reporters obtained a quote from the research from Hebei University of E ngineering: “Realtime semantic segmentation for urban scenes necessitates a bal ance between accuracy, inference speed, and model size. In this paper, we presen t an efficient solution to this challenge, efficient asymmetric attention module net (EAAMNet) for the semantic segmentation of urban scenes, which adopts an as ymmetric encoder-decoder structure. The encoder part of the network utilizes an efficient asymmetric attention module to form the network backbone. In the decod ing part, we propose a lightweight multi-feature fusion decoder that can maintai n good segmentation accuracy with a small number of parameters. Our extensive ev aluations demonstrate that EAAMNet achieves a favorable equilibrium between segm entation efficiency, model parameters, and segmentation accuracy, rendering it h ighly suitable for real-time semantic segmentation in urban scenes.”
Hebei University of EngineeringHebeiPeople’s Republic of ChinaAsiaComputational IntelligenceMachine Learning
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