摘要
由一名新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于公司智能的新报告。据NewsRx记者报道，“目前，已经提出了许多高精度的语义分割模型，但模式L参数大，分割速度慢。”新闻记者引用河北工程大学的一篇研究文章:“城市场景的实时语义分割需要精度、推理速度和模型大小之间的平衡,本文提出了一种有效的解决方案,即高效的非对称注意模块net(EAAMNet),用于城市场景的语义分割。”在编码部分,我们提出了一种轻量级的多特征融合译码器,该译码器可以在较少的参数下保持良好的分割精度,实验结果表明,EAAMNet在分割效率、模型参数和分割精度之间取得了良好的平衡。该算法非常适合城市场景的实时语义分割。
Abstract
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.”
NETL
NSTL