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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders |
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| Sanghyun Woo, Shoubhik Debnath, Ronghang Hu |
| 2023 |
| arxiv 2301.00808 |
| Semantic Representation and Dependency Learning for Multi-Label Image Recognition | Tao Pu, Mingzhan Sun, Hefeng Wu | 2022 | arxiv 2204.03795 |
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| STAIR: Learning Sparse Text and Image Representation in Grounded Tokens | Chen Chen, Bowen Zhang, Liangliang Cao | 2023 | arxiv 2301.13081 |
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| Deep Dependency Networks for Multi-Label Classification | Shivvrat Arya, Yu Xiang, Vibhav Gogate | 2023 | arxiv 2302.00633 |
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| Towards Local Visual Modeling for Image Captioning | Yiwei Ma, Jiayi Ji, Xiaoshuai Sun | 2023 | arxiv 2302.06098 |
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| Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis | Haoran Sun, Yang Wang, Haipeng Liu | 2023 | arxiv 2302.08706 |
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| Using Semantic Information for Defining and Detecting OOD Inputs | Ramneet Kaur, Xiayan Ji, Souradeep Dutta | 2023 | arxiv 2302.11019 |
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| APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | Anneliese Braunegg, Amartya Chakraborty, Michael Krumdick | 2019 | arxiv 1912.08166 |
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| Will Large-scale Generative Models Corrupt Future Datasets? | Ryuichiro Hataya, Han Bao, Hiromi Arai | 2022 | arxiv 2211.08095 |
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| Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes | Di Wu, Pengfei Chen, Xuehui Yu | 2023 | arxiv 2307.12101 |
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