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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation |
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| Jia Liu, Quan Zhou, Yong Qiang |
| 2019 |
| arxiv 1911.00632 |
| Parting with Illusions about Deep Active Learning | Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek | 2019 | arxiv 1912.05361 |
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| 3D-BEVIS: Bird's-Eye-View Instance Segmentation | Cathrin Elich, Francis Engelmann, Theodora Kontogianni | 2019 | arxiv 1904.02199 |
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| Mask Based Unsupervised Content Transfer | Ron Mokady, Sagie Benaim, Lior Wolf | 2019 | arxiv 1906.06558 |
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| Dynamic SLAM: The Need For Speed | Mina Henein, Jun Zhang, Robert Mahony | 2020 | arxiv 2002.08584 |
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| Semantically-Guided Representation Learning for Self-Supervised Monocular Depth | Vitor Guizilini, Rui Hou, Jie Li | 2020 | arxiv 2002.12319 |
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| Train, Learn, Expand, Repeat | Abhijeet Parida, Aadhithya Sankar, Rami Eisawy | 2020 | arxiv 2003.08469 |
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| Structured Knowledge Distillation for Dense Prediction | Yifan Liu, Changyong Shun, Jingdong Wang | 2019 | arxiv 1903.04197 |
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| Instance segmentation of buildings using keypoints | Qingyu Li, Lichao Mou, Yuansheng Hua | 2020 | arxiv 2006.03858 |
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| Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning | Qiqi Xiao, Jiaxu Zou, Muqiao Yang | 2020 | arxiv 2007.13854 |
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