This challenging task is referred to as cross-domain few-shot semantic segmentation (CD-FSS). A novel model, namely FTDCNet, is proposed, which comprises a domain-agnostic feature transformation module and a do
小样本分割来源于小样本学习,因此,也存在小样本学习中常见的问题,作者针对其中跨域样本问题进行了研究,由于跨域样本之间存在域偏移,因此作者提出了 PATNet(Pyramid-Anchor-Transformation based few-shot segmentation network),下图是作者方法与传统方法的对比图: 方法对比图 作者的方法不同之处主要在于,在训练阶段是否会...
Paper tables with annotated results for Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation
This is the implementation of the paper "Cross-Domain Few-Shot Semantic Segmentation". For more information, check out the [paper] and [supp].IntroductionThe Cross-Domain Few-Shot Semantic Segmentation includes data from the Deepglobe [1], ISIC2018 [2-3], Chest X-ray [4-5], and FSS-100...
A broader study of cross-domain few-shot learning Transferring cross-domain knowledge for video sign language recognition Classes matter: A fine-grained adversarial approach to cross-domain semantic segmentation On the limits of cross-domain generalization in automated X-ray prediction Cross-domain semant...
Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the suppo
In this paper, we focus on the matching mechanism of the few-shot semantic segmentation models and introduce an Earth Mover's Distance (EMD) calculation based domain robust matching mechanism for the cross-domain scenario. Specifically, we formulate the EMD transportation process between the ...
Knowledge transduction for cross-domain few-shot learning 2023, Pattern Recognition Show abstract Cross-modal hashing with missing labels 2023, Neural Networks Show abstract Improving pseudo labels with intra-class similarity for unsupervised domain adaptation 2023, Pattern Recognition Show abstract Prototype...
为了解决这一问题,提出了小样本学习(Few-shot learning,FSL)。然而,FSL 假设所有样本(包括源任务和目标任务的数据,目标任务是利用源任务的先验知识进行的)都来自同一领域,这在现实世界中是一个严格的假设。为了缓解这一限制,跨域小样本学习(Cross-domain Few-shot learning,CDFSL)引起了关注,因为它允许源数据和目标...
These works operate under the regime of few-shot target domain scene adaptation. For example, [1, 2] (C3 in Tab. 1) use meta-learning approaches [66] and adapt to the target domain with few scenes for anomaly detection. In contrast, zxVAD is specifically ...