上图展示了source-free domain adaptation和一般的DA的区别。在之前的两篇source-free的论文中已经反复讲解,不再赘述。 1 方法 这文章也是使用Positive learning和Negative Learning的方法。 方法名称:Source-Free domain adaptive Semantic Segmentation (SFSS) 1.1 Notations and Definations SFSS的训练分成两个步骤(和...
我们的主要贡献可概括如下: •我们提出了一种新的SFDA框架,该框架将知识转移和模型自适应相结合,而不需要任何源数据和目标标签。据我们所知,这是首次尝试解决用于语义分割的无源UDA问题。 •专门为分割设计了一种新的双注意力提取机制,以转移和保留上下文信息,并引入了域内补丁级自我监督模块,以利用目标域中的补...
对于分割任务,模型的自适应是解决源域数据缺失的可行方案之一。其中这个研究“Source-free domain adaptation for semantic segmentation”利用生成模型来人工生成虚假的样本来作为源于数据的估计。然后通过model adaptation来实现知识的迁移。 1.3 半监督学习 半监督学习的关键是学习标注样本和未标注样本之间的特征表达的一致...
在之前的两篇source-free的论文中已经反复讲解,不再赘述。 1 方法 这文章也是使用Positive learning和Negative Learning的方法。 方法名称:Source-Free domain adaptive Semantic Segmentation (SFSS) 1.1 Notations and Definations SFSS的训练分成两个步骤(和其他的source-free的算法一样)“ 先在有标注的source data...
To address data privacy concerns in real-world applications, source-free domain adaptation for semantic segmentation (SFSS) has recently been studied, eliminating the need for direct access to source data. Most SFSS methods primarily utilize pseudo-labels to regularize the model in either the ...
This paper addresses an interesting yet challenging problem-source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation-given only a pinhole image-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is nontrivial ...
Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the sour...
We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled target domain without any access to source data; the available information is a model trained to achieve good performance on...
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...
Semantic Segmentation Editor - Hitachi's Open source tool for labelling camera and LIDAR data. Snorkel - Snorkel is a system for quickly generating training data with weak supervision. Superintendent - superintendent provides an ipywidget-based interactive labelling tool for your data. YData Synthetic...