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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Hexiang Hu, Zhiwei Deng, Guang-tong Zhou |
| 2016 |
| arxiv 1611.08061 |
| Improving Fully Convolution Network for Semantic Segmentation | Bing Shuai, Ting Liu, Gang Wang | 2016 | arxiv 1611.08986 |
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| Dense Recurrent Neural Networks for Scene Labeling | Heng Fan, Haibin Ling | 2018 | arxiv 1801.06831 |
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| Skip-Attention: Improving Vision Transformers by Paying Less Attention | Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano | 2023 | arxiv 2301.02240 |
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| Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation | Maksym Yatsura, Kaspar Sakmann, N. Grace Hua | 2022 | arxiv 2209.05980 |
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| Seeing Through Clutter: Structured 3D Scene Reconstruction via Iterative Object Removal | Rio Aguina-Kang, Kevin James Blackburn-Matzen, Thibault Groueix | 2026 | arxiv 2602.04053 |
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| Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning | Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed | 2022 | arxiv 2212.04994 |
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| Open-Vocabulary Universal Image Segmentation with MaskCLIP | Zheng Ding, Jieke Wang, Zhuowen Tu | 2022 | arxiv 2208.08984 |
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| Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct? | Ping Luo, Zhanglin Peng, Jiamin Ren | 2018 | arxiv 1811.07727 |
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| Pretraining is All You Need for Image-to-Image Translation | Tengfei Wang, Ting Zhang, Bo Zhang | 2022 | arxiv 2205.12952 |
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