"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...
机器学习模型是在一个封闭的数据集上训练的,然而其应用的场景的数据分布往往与训练数据存在很大差异,会使得模型的性能出现很大的drop,这就需要对模型做领域自适应(DA,Domain Adaptation)。如果模型被应用多个不同的场景,涉及到的就是多目标领域自适应(MTDA,Multi-Target Domain Adaptation)。本文提出了一种被称之为CGC...
两个模态的数据分别经过I3D卷积模型FRGBandFFlowFRGBandFFlow提取特征,生成一个向量,一方面将该向量通过GRL层(起到一个反向传播的作用,方便训练)到达域判别器,一方面将该向量输入到Classification部分(FC+softmax)。我们的目标是拉近source与target域,且将视频尽量分类正确,所以最大化LFlowdandLRGBdLdFlowandLdRGB和...
Cross-domain representation-decoupledEasy-to-hardMulti-target domain adaptationMutual informationProgressive strategy2023 Elsevier Inc.Transferring knowledge from one labeled source domain to unlabeled multi-target domains is of great challenge in unsupervised domain adaptation. Within multi-target domains, 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...
Moreover, this approach becomes more time and computationally intensive as the number of unlabeled target domains keeps on growing. Second, the existing STDA methods for reID do not emphasize the performance on the source domain after adaptation. The model performance drops on the source domain ...
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021) - Evgeneus/Graph-Domain-Adaptaion
Figure 1 presents an illustration of domain shift. While the classifier can be effectively trained using the labeled source domain data, it loses the classification validity on the target domain due to the existence of domain shift. That leads to serious performance degradation in fault ...
Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain. ...
: We formulate the facial emotion recognition with scarce data as a multi-source domain adaptation (MSDA) problem, in which there are N labelled source domains and one target domain with few labelled samples. Let the input space be X