Therefore, the simultaneous achievement of high efficiency and high accuracy is challenging and of great importance for exploiting a specific architecture for the real-time semantic segmentation task. 二、解决的办法(What) 低层信息和高层语义对语义分割同样重要,然鹅两种信息对模型结构的要求不太一样。低层...
我们提出了一种高效的实时语义分割双分辨率TransformerRTprorr,它比基于CNN的模型在性能和效率之间实现更好的权衡。为了在GPU这类设备上实现高推理效率,我们的RTformer利用了线性复杂度的GPU友好注意力,并抛弃了多头机制。此外,我们发现交叉分辨率注意通过传播从低分辨率分支获得的高级知识,可以更有效地收集高分辨率分支的...
论文导读:本文选自CVPR2021,提出了一种新的,实时的,语义分割网络。 Abstract: 本文提出了一种新的实时语义分割网络。主要贡献如下: 提出了一种新的超网络架构,在U-Net中使用-UNet。 提出新颖的动态patch-wise卷积,使每个输入和每个空间位置的权值都不同。 Method: Overview 所提出的超网络编解码器方法如图2所示。
realtime semantic segmentation. To this end, we propose an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2). This architecture involves: (i) a Detail Branch, with wide channels and shallow layers to capture ...
Real-time Semantic Segmentation Comparative Study The repository contains the official TensorFlow code used in our papers: RTSEG: REAL-TIME SEMANTIC SEGMENTATION COMPARATIVE STUDY for comparing different realtime semantic segmentation architectures. SHUFFLESEG: REAL-TIME SEMANTIC SEGMENTATION NETWORK which introd...
Fast Semantic Segmentation 快速语义分割在对象检测中,速度成为系统设计中的一个重要因素。近来 Yolo 和SSD 大大改善了它。相反,在语义分割中研究高推断速度的研究刚刚开始。 SegNet 放弃一些层来减少层参数,ENet是一个轻量级的网络。这些方法大大提高了效率。但准确性令人担忧。
Real-time semantic segmentation is a crucial component of autonomous driving systems, where accurate and efficient scene interpretation is essential to ensure both safety and operational reliability. This review provides an in-depth analysis of state-of-the-art approaches in real-time semantic ...
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Bisenet: Bilateral segmentation network for real-time semantic segmentation. 2018.C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang. Bisenet: Bilateral segmentation network for real-time semantic ...
Rethinking BiSeNet For Real-time Semantic Segmentation 一言以蔽之:提模块,弃支路,想法和效果都能打 1.Abstract BiSeNet是实时分割领域主流的双流(two-stream)网络。然而BiSeNet增加支路去编码空间信息的做法是耗时的,而且直接使用诸如分类等预训练任务的骨干网络进行分割任务是不合理的。为了解决以上问题,我们移除了冗余...
CVPR2019|In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of ... 用于道路驾驶的实时语义分割 Abstract 在要求苛刻的道路驱动数据集上, 语义分割方法最近取得了成功, 激发了人们对许多相关应用领域的兴趣。其中许多应用涉及汽车、无人机和各种机器人等移动平台上的实时预测。