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 …
[HTML][HTML] Deep-learning-based approaches for semantic segmentation of natural scene images: A review
The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …
Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection
This study addresses the issue of fusing infrared and visible images that appear differently
for object detection. Aiming at generating an image of high visual quality, previous …
for object detection. Aiming at generating an image of high visual quality, previous …
Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion
Image fusion integrates a series of images acquired from different sensors, eg, infrared and
visible, outputting an image with richer information than either one. Traditional and recent …
visible, outputting an image with richer information than either one. Traditional and recent …
Reconet: Recurrent correction network for fast and efficient multi-modality image fusion
Recent advances in deep networks have gained great attention in infrared and visible image
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
Affinity attention graph neural network for weakly supervised semantic segmentation
Weakly supervised semantic segmentation is receiving great attention due to its low human
annotation cost. In this paper, we aim to tackle bounding box supervised semantic …
annotation cost. In this paper, we aim to tackle bounding box supervised semantic …
Coconet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion
Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …
complementary information from different sensors. Existing learning-based fusion …
Context-aware feature generation for zero-shot semantic segmentation
Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To
reduce the annotation pressure, we focus on a challenging task named zero-shot semantic …
reduce the annotation pressure, we focus on a challenging task named zero-shot semantic …
Sparsely annotated semantic segmentation with adaptive gaussian mixtures
Sparsely annotated semantic segmentation (SASS) aims to learn a segmentation model by
images with sparse labels (ie, points or scribbles). Existing methods mainly focus on …
images with sparse labels (ie, points or scribbles). Existing methods mainly focus on …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …