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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Distilling Inter-Class Distance for Semantic Segmentation | Zhengbo Zhang, Chunluan Zhou, Zhigang Tu | 2022 | arxiv 2205.03650 |
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| What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance | Mahmoud Afifi, Michael S Brown | 2019 | arxiv 1912.06960 |
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| Bayesian Active Learning for Semantic Segmentation | Sima Didari, Wenjun Hu, Jae Oh Woo | 2024 | arxiv 2408.01694 |
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| MacFormer: Semantic Segmentation with Fine Object Boundaries | Guoan Xu, Wenfeng Huang, Tao Wu | 2024 | arxiv 2408.05699 |
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| Contextrast: Contextual Contrastive Learning for Semantic Segmentation | Changki Sung, Wanhee Kim, Jungho An | 2024 | arxiv 2404.10633 |
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| Continual Segmentation with Disentangled Objectness Learning and Class Recognition | Yizheng Gong, Siyue Yu, Xiaoyang Wang | 2024 | arxiv 2403.03477 |
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| Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | Jiarui Xu, Sifei Liu, Arash Vahdat | 2023 | arxiv 2303.04803 |
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| ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation | Shuyang Sun, Weijun Wang, Qihang Yu | 2023 | arxiv 2306.17319 |
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| Open-vocabulary Panoptic Segmentation with Embedding Modulation | Xi Chen, Shuang Li, Ser-Nam Lim | 2023 | arxiv 2303.11324 |
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| Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers | Alberto Gonzalo Rodriguez Salgado, Maying Shen, Philipp Harzig | 2024 | arxiv 2412.01941 |
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