… has been greatly improved by using deep learning … efforts on deep-learning-based semantic
segmentation methods. We … of semantic segmentation research in the deep learning era. …

… semantic segmentation and present their network structures and key ideas. This section
also … deep learning architectures that are not directly applicable to semantic segmentation …

… a novel deep fully convolutional network for semantic segmentation, called CGNet. The key
idea of CGNet is to guide … Xception: deep learning with depthwise separable convolutions. In: …

… the data as per requirements, advanced deep learning models are required that must be …
The objective of this work is the semantic segmentation of PolSAR data using a deep learning …

… deep learning and knowledge-guided ontology reasoning for RS image semantic segmentation,
… It not only adopts the DSSN to learn the low-level and mid-level cues from RS images, …

… behind many structured machine learning models [20, 85], … semantic segmentation literature,
surprisingly little is understood about how to accommodate pixel recognition into semantic …

… Semantic segmentation is a classic and fundamental topic in … deep learning greatly promotes
the performance of semantic … For the final semantic segmentation prediction, we adopt …

Semantic segmentation is an important field for automatic processing of remote sensing image
… To obtain more accurate segmentation results, this paper introduces edge information as …

… We aimed to use deep learning to reliably and efficiently quantify and detect different lesion
… We searched PubMed for machine learning or deep learning studies focusing on automated …

… of deep learning (DL), modified UNet semantic segmentation using convolutional neural
networks (… This study does not attempt to develop a new DL semantic segmentation algorithm or …