论文名称:Rethinking BiSeNet For Real-time Semantic Segmentation 论文链接:Rethinking BiSeNet for Real-Time Semantic Segmentation (thecvf.com)一、Introduction1 Motivation:在目前的实时语义分割网络中…
classSTDCNet(BaseModule):"""This backbone is the implementation of `Rethinking BiSeNet For Real-time Semantic Segmentation <https://arxiv.org/abs/2104.13188>`_. Args: stdc_type (int): The type of backbone structure, `STDCNet1` and`STDCNet2` denotes two main backbones in paper, whose FLO...
实时语义分割:STDC Rethinking BiSeNet For Real-time Semantic Segmentation CVPR 2021 · Mingyuan Fan, Shenqi Lai, Junshi Huang, Xiaoming Wei, Zhenhua Chai, Junfeng Luo, Xiaolin Wei · Edit social preview BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However...
2104.13188:Rethinking BiSeNet For Real-time Semantic Segmentation 创新点 Short-Term Dense Concatenate(STDC): 在BiSeNet(context path + spatial path)的基础上,对有效但极耗时的 spatial path 进行了 去冗余 。 逐步降低特征图的维度,并利用它们的聚合来表示图像,以此形成 STDC 网络的基本模块。 在解码器中,通...
该方法可完整识别轨道区域，轨道被准确地分割且边缘轮廓完整准确。关键词：井下轨道区域；语义分割；短期密集连接网络；特征注意力；特征融合；注意力机制 中图分类号：TD67 文献标志码：A Real time segmentation method for underground track area based on improved STDC MA Tian 1, LI Fanhui 1, Y...
wget http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/segmentation/stdc/stdc1_cityscapes/epoch_1250_export.pthfrom easycv.predictors.segmentation import SegmentationPredictoroutput_ckpt = 'epoch_1250_export.pth'detector = SegmentationPredictor(output_ckpt)output = detector(['sma...
Aiming at the problems of poor edge information segmentation and low real-time performance in current underground track area segmentation methods, a real-time track area segmentation method based on improved network short-term dense concatenate (STDC) is proposed. STDC is adopted ...
Project for "Advanced Machine Learning" course at PoliTO. The purpose is to implement a BiSeNet able to perform real-time semantic segmentation task autonomous-drivingstdcdepthwise-separable-convolutionsunsupervised-domain-adaptationbisenetreal-time-semantic-segmentation ...
在STDC-Seg(Short-Term Dense Concatenate Segmentation)网络中,设计了一个轻量级的STDC Backbone来提取特征。它消除了分支上的特征冗余,并利用来自GT的边缘细节信息来指导空间特征学习。STDC-Seg网络在精度和速度上都取得了令人满意的结果;但是它没有考虑不同尺度图像对网络的影响。
LPSNet 在多个数据集上优于多种有效的语义分割方法。特别在 Cityscapes 测试集上，其性能显著提升，展现出在轻量级与高性能之间的出色平衡。这些研究成果为语义分割领域的轻量级与高效网络设计提供了新思路。参考文献：[1].Lightweight and Progressively-Scalable Networks for Semantic Segmentation ...