鉴别器的网络包含5个卷积层,kernel大小为4×4,通道数分别为[64,128,256,512,1], stride为 2。 参考文献: [1] Kim M , H Byun. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation[C]// 2020. 编辑于 2021-08-28 10:00 内容所属专栏 Domain Adaptation 订阅专栏...
参考文献: [1]Chen M , Xue H , Cai D . Domain Adaptation for Semantic Segmentation With Maximum Squares Loss[C]// International Conference on Computer Vision. 0.
The success of CNNs for semantic segmentation depends heavily on the pixel-level ground truth, which is labor-intensive in general. To partially solve this problem, domain adaptation techniques have been adapted to the two similar tasks for semantic segmentation, one of which is fully-labelled, ...
(2) 我们设计了一个名为MADAN的新框架来进行语义分割的MDA。除了特征级对齐之外,还通过为每个源循环生成一个自适应域来进一步考虑像素级对齐,该域与新的动态语义一致性损失保持一致。提出了子域聚合鉴别器和跨域循环鉴别器,以更好地对齐不同的自适应域。(3) 我们从合成的GTA和SYNTHIA到真实的Cityscapes和BDDS数据...
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training 目录 问题 方法 使用自步学习的自监督训练 类别平衡自监督训练 自步学习过程设计 空间先验 问题 主要解决的问题是自监督训练中,伪标签的质量问题。 方法 提出了一种基于迭代自训练过程的UDA框架,将问题表示为隐藏变量损失最小化,可以通过...
1、ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation 目的:这篇文章和前几篇一样的思想,都是用对抗学习的思想来做域自适应学习分割,一个系列的文章,前面几篇可以当做了解,这篇着重学习代码,以及熵最小化这种思想。从网络,训练过程多方面学习,尤其把网络训练起来。 Adversar....
Domain adaptationRGB-depthMulti-task learningMost contemporary robots have depth sensors, and research on semantic segmentation with RGBD images has shown that depth images boost the accuracy of segmentation. Since it is time-consuming to annotate images with semantic labels per pixel, it would be ...
论文阅读 | A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes paper链接:https://arxiv.org/pdf/1812.09953.pdf code链接:https://github.com/YangZhang4065/AdaptationSeg 摘要: 在过去的5年里面,卷积神经网络在语义分割领域大获全胜,语义分割是许多其他应用的核心任务之一,这...
网络:translation model(F) is CycleGAN, DeepLab V2 with the backbone ResNet101 and FCN-8s with VGG16 as our segmentation model. 代码:https://github.com/liyunsheng13/BDL 本篇论文是基于Cycle-consistent adversarialdomain adaptation(CyCADA)提出的子网络框架,一个子网络是F,一个是M。
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target do-main. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-wo...