Dec 10, 2021 · Semisupervised semantic segmentation is an effective way to reduce the expensive manual annotation cost and take advantage of the unlabeled data for remote ...
The proposed model in the generative adversarial network (GAN) framework is optimized by consistency self-training, learning the distributions of both labeled ...
Apr 19, 2023 · We propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure.
Semi-supervised semantic segmentation (SSS) of RS images would be a promising solution, which uses both limited labeled data and dominant unlabeled data to ...
People also ask
What is semi supervised segmentation?
What is an example of semantic segmentation of an image?
What is image segmentation in remote sensing?
What is the difference between image segmentation and semantic segmentation?
This paper introduces a novel semi-supervised semantic segmentation framework based on Mean Teacher (MT).
Sep 4, 2024 · To alleviate this problem, this letter proposes a semi-supervised segmentation method of remote sensing images based on an iterative contrastive ...
This paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence.
Mar 31, 2024 · In addition, the proposed method combines self-training and consistency regularization, which uses their predictions to supervise itself and ...
Oct 22, 2024 · Semi-supervised learning is a forcible method to lessen the cost of annotation for remote sensing semantic segmentation tasks.
Oct 22, 2024 · This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs.