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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection | Fen Xiao, Wenzheng Deng, Liangchan Peng | 2018 | arxiv 1801.04187 |
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| OD-VIRAT: A Large-Scale Benchmark for Object Detection in Realistic Surveillance Environments | Hayat Ullah, Abbas Khan, Arslan Munir | 2025 | arxiv 2507.12396 |
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| Improving Object Detection Quality in Football Through Super-Resolution Techniques | Karolina Seweryn, Gabriel Chęć, Szymon \Lukasik | 2024 | arxiv 2402.00163 |
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| Self-Supervised Learning for Real-World Object Detection: a Survey | Alina Ciocarlan, Sidonie Lefebvre, Sylvie Le Hégarat-Mascle | 2024 | arxiv 2410.07442 |
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| Self-Supervised Object Detection via Generative Image Synthesis | Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash | 2021 | arxiv 2110.09848 |
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| Rethinking the backbone architecture for tiny object detection | Jinlai Ning, Haoyan Guan, Michael Spratling | 2023 | arxiv 2303.11267 |
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| DynamicDet: A Unified Dynamic Architecture for Object Detection | Zhihao Lin, Yongtao Wang, Jinhe Zhang | 2023 | arxiv 2304.05552 |
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| Object-Centric Stereo Matching for 3D Object Detection | Alex D. Pon, Jason Ku, Chengyao Li | 2019 | arxiv 1909.07566 |
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| Salvage of Supervision in Weakly Supervised Object Detection | Lin Sui, Chen-Lin Zhang, Jianxin Wu | 2021 | arxiv 2106.04073 |
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| CaT: Weakly Supervised Object Detection with Category Transfer | Tianyue Cao, Lianyu Du, Xiaoyun Zhang | 2021 | arxiv 2108.07487 |
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