Domain adaptive object detectionMulti-source domain adaptationSource-free domain adaptationContrastive learningTo enhance the transferability of object detection models in real-world scenarios where data is sampled from disparate distributions, considerable attention has been devoted to domain adaptive object ...
AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identi...
(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 adaptationMulti-source learningMaximum mean discrepancySample reweightingIn recent years, domain adaptation and transfer learning are known as promising techniques with admirable performance to deal with problems with distribution difference between the training (source domain) and test (target domain)...
今天介绍一篇关于行人重识别UDA的方法,“Unsupervised Multi-Source Domain Adaptation for Person Re-Identifification ” 论文地址: https://arxiv.org/pdf/2104.12961.pdfarxiv.org/pdf/2104.12961.pdf Motion 以前UDA的方法,都是从一个标记了数据的域获取信息来适应另一个无标签的域。但是reid发展至今,有...
With the availability of multiple datasets, Multi-Source Domain Adaptation (MSDA)13 has gained interest, wherein multiple labelled source domains are used to transfer the learnt knowledge to the target domain. The generalizability of multi-source transfer learning in providing a broader view of the ...
这篇文章 [1]比较新,今年七月刚挂在arxiv上面的,感觉比较有意思,就拿来记录一下。文章想要解决的问题是multi-source下的open-set domain adaptation[2],提出了一种叫做HyMOS(Hyperspherical Multi-source Ope…
Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. In the former, partial knowledge is transferred from multiple source domains to the target domain and fused therein. Negative transfer between unmatched label space is mitigated via three new modules...
Therefore, we propose an effective source-free unsupervised domain adaptation method for cross-modality abdominal multi-organ segmentation without source dataset access. The proposed framework comprises two stages. In the first stage, the feature map statistics-guided model adaptation combined with entropy...
Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to ...