Review the state-of-the-art technologies of semantic segmentation based on deep learning
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …
costly process, a model can instead be trained with more accessible synthetic data and …
Hrda: Context-aware high-resolution domain-adaptive semantic segmentation
Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding
Level 5 autonomy for self-driving cars requires a robust visual perception system that can
parse input images under any visual condition. However, existing semantic segmentation …
parse input images under any visual condition. However, existing semantic segmentation …
Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation
Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …
LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation
Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
Self-supervised augmentation consistency for adapting semantic segmentation
N Araslanov, S Roth - … of the IEEE/CVF conference on …, 2021 - openaccess.thecvf.com
We propose an approach to domain adaptation for semantic segmentation that is both
practical and highly accurate. In contrast to previous work, we abandon the use of …
practical and highly accurate. In contrast to previous work, we abandon the use of …
Fda: Fourier domain adaptation for semantic segmentation
We describe a simple method for unsupervised domain adaptation, whereby the
discrepancy between the source and target distributions is reduced by swapping the low …
discrepancy between the source and target distributions is reduced by swapping the low …