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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2023 |
| arxiv 2311.09269 |
| Where to look first? Behaviour control for fetch-and-carry missions of service robots | Markus Bajones, Daniel Wolf, Johann Prankl | 2015 | arxiv 1510.01554 |
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| Pushing the Boundaries of Boundary Detection using Deep Learning | Iasonas Kokkinos | 2015 | arxiv 1511.07386 |
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| Re-ranking Object Proposals for Object Detection in Automatic Driving | Zhun Zhong, Mingyi Lei, Shaozi Li | 2016 | arxiv 1605.05904 |
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| Adapting Deep Network Features to Capture Psychological Representations | Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths | 2016 | arxiv 1608.02164 |
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| Learning Dynamic Hierarchical Models for Anytime Scene Labeling | Buyu Liu, Xuming He | 2016 | arxiv 1608.03474 |
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| Higher Order Conditional Random Fields in Deep Neural Networks | Anurag Arnab, Sadeep Jayasumana, Shuai Zheng | 2015 | arxiv 1511.08119 |
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| Can fully convolutional networks perform well for general image restoration problems? | Subhajit Chaudhury, Hiya Roy | 2016 | arxiv 1611.04481 |
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| Semantic Video CNNs through Representation Warping | Raghudeep Gadde, Varun Jampani, Peter V. Gehler | 2017 | arxiv 1708.03088 |
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| RoomNet: End-to-End Room Layout Estimation | Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz | 2017 | arxiv 1703.06241 |
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| What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? | Alex Kendall, Yarin Gal | 2017 | arxiv 1703.04977 |
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