SOTA2 Research · papers

Papers for “semantic segmentation”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

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20 indexed

Title	Authors	Year	Source
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis	Xihui Liu, Guojun Yin, Jing Shao	2019	arxiv 1910.06809
Boosting Real-Time Driving Scene Parsing with Shared Semantics	Zhenzhen Xiang, Anbo Bao, Jie Li	2019	arxiv 1909.07038
Meta Segmentation Network for Ultra-Resolution Medical Images	Tong Wu, Yuan Xie, Yanyun Qu	2020	arxiv 2002.08043
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	Shaokai Ye, Kailu Wu, Mu Zhou	2019	arxiv 1911.12796
CAFENet: Class-Agnostic Few-Shot Edge Detection Network	Young-Hyun Park, Jun Seo, Jaekyun Moon	2020	arxiv 2003.08235
Post-Training Piecewise Linear Quantization for Deep Neural Networks	Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz	2020	arxiv 2002.00104
Oriented Boxes for Accurate Instance Segmentation	Patrick Follmann, Rebecca König	2019	arxiv 1911.07732
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds	Huan Lei, Naveed Akhtar, Ajmal Mian	2019	arxiv 1909.09287
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions	Johanna Wald, Helisa Dhamo, Nassir Navab	2020	arxiv 2004.03967
Accelerating Neural Network Inference by Overflow Aware Quantization	Hongwei Xie, Shuo Zhang, Huanghao Ding	2020	arxiv 2005.13297
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes