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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Xiaoshi Wu, Feng Zhu, Rui Zhao |
| 2023 |
| arxiv 2303.13076 |
| Box-Level Active Detection | Mengyao Lyu, Jundong Zhou, Hui Chen | 2023 | arxiv 2303.13089 |
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| Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel Space | Seokju Yun, Youngmin Ro | 2023 | arxiv 2304.07254 |
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| Biologically-plausible learning algorithms can scale to large datasets | Will Xiao, Honglin Chen, Qianli Liao | 2018 | arxiv 1811.03567 |
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| Feature Selective Anchor-Free Module for Single-Shot Object Detection | Chenchen Zhu, Yihui He, Marios Savvides | 2019 | arxiv 1903.00621 |
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| Linear Context Transform Block | Dongsheng Ruan, Jun Wen, Nenggan Zheng | 2019 | arxiv 1909.03834 |
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| MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection | Xin Lu, Quanquan Li, Buyu Li | 2020 | arxiv 2009.11528 |
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| YOLACT++: Better Real-time Instance Segmentation | Daniel Bolya, Chong Zhou, Fanyi Xiao | 2019 | arxiv 1912.06218 |
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| Improvement of Classification in One-Stage Detector | Wu Kehe, Chen Zuge, Zhang Xiaoliang | 2020 | arxiv 2011.10465 |
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| G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection | Yufan Luo, Li Xiao | 2020 | arxiv 2012.03677 |
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