"ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation "解读 MoveOn 电子信息1 人赞同了该文章 研究背景和意义 图1 现有多目标域自适应方法 大多数以往多目标域泛化算法都是采用单目标域模型风格化迁移不同目标域数据来构建多目标域模型。 这些方法取得了良好的效果,但其性能...
Domain selection stage:根据信息熵选择最easy的领域,下式选择的是在t_{dcl}^0步所用的目标领域。 Adaptation stage:与CGCT基本相同,只是t_{dcl}^q步做feature adaptation使用的数据集是\hat S^q\cup T_{D^q}而不是所有目标领域T。 Pseudo-labelling stage:这一步依然与CGCT基本相同,只是目标领域的样本是从...
Multi-Target Domain Adaptation (MTDA) is a recently popular powerful setting in which a single classifier is learned for multiple unlabeled target domains. A popular MTDA approach is to sequentially adapt one target domain at a time. While only one pass is made through each target domain, the ...
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extend...
Hence, to tackle this problem of unsupervised multi-target domain adaptation (MTDA), we propose a novel architecture called SSMTReID-Net. SSMTReID-Net employs the elastic weight consolidation (EWC) regularizer to ensure competitive performance on the source domain after adaptation, and the notion ...
Techniques for multi-target domain adaptation (MTDA) seek to adapt a recognition model such that it can generalize well across multiple target domains. While several successful techniques have been proposed for unsupervised single-target domain adaptation (STDA) in object detection, adapting a model ...
This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge distillation (\textit{CoNMix}) as a solution to this problem. The main motive...
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021) - Evgeneus/Graph-Domain-Adaptaion
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to ...
Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories Motivation 到目前为止,已有的研究大多集中在一个源域和一个目标域(1S1T)的场景上,只有少数研究涉及多个源域和一个目标域(mS1T)的场景。然而,据我们所知,几乎没有工作涉及一个源域和多个目标域(1SmT)的情况,在这种情况下,这些未标记...