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Pyramid Scene Parsing Network
TLDR
This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
TLDR
An image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address the challenging task of real-time semantic segmentation is proposed and in-depth analysis of the framework is provided.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
TLDR
This work proposes a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D Dense UNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
TLDR
An efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework and can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating.
Image Inpainting via Generative Multi-column Convolutional Neural Networks
TLDR
A generative multi-column network that synthesizes different image components in a parallel manner within one stage to better characterize global structures while an implicit diversified MRF regularization is adopted to enhance local details.
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
TLDR
The proposed Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image achieves top performance on surface normal estimation and is on par with state-of-the-art depth estimation methods.
Semi-Parametric Image Synthesis
TLDR
A semi-parametric approach to photographic image synthesis from semantic layouts that combines the complementary strengths of parametric and nonparametric techniques is presented.
Deep Contextual Networks for Neuronal Structure Segmentation
TLDR
A deep contextual network is proposed here by leveraging multi-level contextual information from the deep hierarchical structure to achieve better segmentation performance and can potentially facilitate the automatic connectome analysis from EM images with less human intervention effort.
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