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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 survey on deep learning and its applications
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …
MIC: Masked image consistency for context-enhanced domain adaptation
In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …
adapted to target data (eg real-world) without access to target annotation. Most previous …
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 …
Semi-supervised semantic segmentation with cross-consistency training
In this paper, we present a novel cross-consistency based semi-supervised approach for
semantic segmentation. Consistency training has proven to be a powerful semi-supervised …
semantic segmentation. Consistency training has proven to be a powerful semi-supervised …