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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation |
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| Haitong Tang, Shuang He, Mengduo Yang |
| 2021 |
| arxiv 2108.00408 |
| A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation | Prashant Pandey, Mustafa Chasmai, Monish Natarajan | 2023 | arxiv 2302.14163 |
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| Semantic segmentation of trajectories with agent models | Daisuke Ogawa, Toru Tamaki, Bisser Raytchev | 2018 | arxiv 1802.09659 |
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| Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation | Siqi Fan, Fenghua Zhu, Zunlei Feng | 2022 | arxiv 2211.16701 |
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| Learning to Adapt Structured Output Space for Semantic Segmentation | Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter | 2018 | arxiv 1802.10349 |
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| Learning to Predict Context-adaptive Convolution for Semantic Segmentation | Jianbo Liu, Junjun He, Jimmy S. Ren | 2020 | arxiv 2004.08222 |
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| Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks | Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan | 2019 | arxiv 1907.09438 |
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| Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation | Runfa Chen, Yu Rong, Shangmin Guo | 2022 | arxiv 2203.07988 |
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| MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks | Xinghao Chen, Yiman Zhang, Yunhe Wang | 2020 | arxiv 2007.08386 |
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| HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation | Xinyu Wang, Jinghua Hou, Zhe Liu | 2025 | arxiv 2507.18575 |
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