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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| arxiv 1706.03646 |
| A Semi-supervised Framework for Image Captioning | Wenhu Chen, Aurelien Lucchi, Thomas Hofmann | 2016 | arxiv 1611.05321 |
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| Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors | Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu | 2017 | arxiv 1707.06399 |
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| Residual Features and Unified Prediction Network for Single Stage Detection | Kyoungmin Lee, Jaeseok Choi, Jisoo Jeong | 2017 | arxiv 1707.05031 |
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| Fraternal Dropout | Konrad Zolna, Devansh Arpit, Dendi Suhubdy | 2017 | arxiv 1711.00066 |
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| Deep Regionlets for Object Detection | Hongyu Xu, Xutao Lv, Xiaoyu Wang | 2017 | arxiv 1712.02408 |
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| SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection | Yonghyun Kim, Bong-Nam Kang, Daijin Kim | 2018 | arxiv 1808.04974 |
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| In-Place Activated BatchNorm for Memory-Optimized Training of DNNs | Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder | 2017 | arxiv 1712.02616 |
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| CE-FPN: Enhancing Channel Information for Object Detection | Yihao Luo, Xiang Cao, Juntao Zhang | 2021 | arxiv 2103.10643 |
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| Exploring Cross-Image Pixel Contrast for Semantic Segmentation | Wenguan Wang, Tianfei Zhou, Fisher Yu | 2021 | arxiv 2101.11939 |
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| Deep Gaussian Processes for Few-Shot Segmentation | Joakim Johnander, Johan Edstedt, Martin Danelljan | 2021 | arxiv 2103.16549 |
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