147 citations · 199 across the 9 of their papers we have counts for
9 papers
Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT
Qinghui Liu, Bradley J MacIntosh, Till Schellhorn +3
We propose a novel and flexible attention based U-Net architecture referred to as "Voxels-Intersecting Along Orthogonal Levels Attention U-Net" (viola-Unet), for intracranial hemor…
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Multi-modality data is becoming readily available in remote sensing (RS) and can provide complementary information about the Earth's surface. Effective fusion of multi-modal inform…
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this effici…
The 1st Agriculture-Vision Challenge: Methods and Results
Mang Tik Chiu, Xingqian Xu, Kai Wang +39
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we…
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Co…
Dense Dilated Convolutions Merging Network for Land Cover Classification
Qinghui Liu, Michael Kampffmeyer, Robert Jessen +1
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotat…
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutio…
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and co…