Few-shot semantic segmentation aims at learning to segment a novel object class with only a few annotated examples. Most existing methods consider a setting where base classes are sampled from the same domain as the novel classes. However, in many applications, collecting sufficient training data ...
The Cross-Domain Few-Shot Semantic Segmentation includes data from the Deepglobe [1], ISIC2018 [2-3], Chest X-ray [4-5], and FSS-1000 [6] datasets, which covers satellite images, dermoscopic images of skin lesions, X-ray images, and daily objects respectively. The selected datasets refle...
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 semantic segmentation via domain-invariant interactive relation transfer Cross-domain few-shot classification via learned feature-wi...
Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification. IEEE Trans Image Process , 2020 , 29: 1233 -1245 CrossRef PubMed ADS arXiv Google Scholar [10] Song L, Wang C, Zhang L. Unsupervised domain adaptive re-identification: Theory and practice. Pattern Recognition...
Gap between few-shot learning and domain adaptation Before introducing our method, we first take a closer look at the gap between FSL and UDA. It is widely accepted that FSL can be formulated as meta-learning. While meta training, samples are randomly selected to construct tens of thousands ...
2018Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training 文章目录 一、简单介绍 一、简单介绍 我先说自己找这篇文章的目的:Self-Training的方法看过很多,想从类别平衡这个角度上去找找思路,看完摘要,有几个地方先记录下:啥叫隐变量损失最小化,另外大的类别(比如road,占图像...
Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems, pp 4077–4087 Chen C et al (2019) Progressive feature alignment for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision an...
Few-Shot Segmentation of Remote Sensing Images Using Deep Metric Learning. IEEE Geosci. Remote Sens. Lett. 2022, 19, 1–5. [Google Scholar] [CrossRef] Osco, L.P.; Marcato Junior, J.; Marques Ramos, A.P.; de Castro Jorge, L.A.; Fatholahi, S.N.; de Andrade Silva, J.; ...
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training. In European Conference on Computer Vision (ECCV 2018); Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar] Tsai, Y.H.; Sohn, K.; Schulter, S.; Chandraker, M. Domain Adaptation for Structured ...
To improve the model’s generalization ability, in the density map estimation phase, we propose a meta-learning-based method, which accelerates the model’s convergence in few-shot scenes with the dynamic meta-learning rate 𝛽β. (2) In cross-domain scenarios, domain-invariant feature extractio...