| Extreme learning machine for multilayer perceptron J Tang, C Deng, GB Huang IEEE Transactions on Neural Networks and Learning Systems, 2015 | 1435 | 2015 |
| Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine J Tang, C Deng, GB Huang, B Zhao IEEE transactions on geoscience and remote sensing 53 (3), 1174-1185, 2014 | 447 | 2014 |
| Extreme learning machines: new trends and applications CW Deng, GB Huang, J Xu, JX Tang Science China information sciences 2 (58), 1-16, 2015 | 150 | 2015 |
| GCNv2: Efficient Correspondence Prediction for Real-Time SLAM J Tang, L Ericson, J Folkesson, P Jensfelt arXiv:1902.11046, 2019 | 119 | 2019 |
| Geometric correspondence network for camera motion estimation J Tang, J Folkesson, P Jensfelt IEEE Robotics and Automation Letters 3 (2), 1010-1017, 2018 | 59 | 2018 |
| Self-supervised 3d keypoint learning for ego-motion estimation J Tang, R Ambrus, V Guizilini, S Pillai, H Kim, P Jensfelt, A Gaidon Conference on Robot Learning, 2085-2103, 2021 | 29 | 2021 |
| A fast learning algorithm for multi-layer extreme learning machine J Tang, C Deng, GB Huang, J Hou 2014 IEEE International Conference on Image Processing (ICIP), 175-178, 2014 | 29 | 2014 |
| Neural outlier rejection for self-supervised keypoint learning J Tang, H Kim, V Guizilini, S Pillai, R Ambrus arXiv preprint arXiv:1912.10615, 2019 | 24 | 2019 |
| Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction J Tang, J Folkesson, P Jensfelt IEEE Robotics and Automation Letters 4 (2), 530 - 537, 2019 | 15 | 2019 |
| Keypoint matching using graph convolutions J Tang, RA Ambrus, J Li, V Guizilini, S Pillai, AD Gaidon US Patent App. 17/231,905, 2021 | 4 | 2021 |
| Systems and methods for training a neural keypoint detection network J Tang, RA Ambrus, V Guizilini, S Pillai, H Kim US Patent 11,256,986, 2022 | 3 | 2022 |
| Incremental map building using learnable features and descriptors J Tang, RA Ambrus, H Kim, V Guizilini, AD Gaidon, W Xipeng, J WALLS, ... US Patent App. 17/230,942, 2021 | 2 | 2021 |
| Self-supervised 3d keypoint learning for monocular visual odometry J Tang, RA Ambrus, V Guizilini, S Pillai, H Kim, AD Gaidon US Patent App. 17/093,393, 2021 | 2 | 2021 |
| Learning algorithms for digital reconstruction of Van Gogh’s drawings Y Zeng, J Tang, JCA van der Lubbe, M Loog Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation …, 2016 | 2 | 2016 |
| Depth estimation based on ego-motion estimation and residual flow estimation J Tang, RA Ambrus, V Guizilini, AD Gaidon US Patent App. 17/230,941, 2021 | 1 | 2021 |
| Semantically aware keypoint matching J Tang, RA Ambrus, V Guizilini, AD Gaidon US Patent App. 17/230,947, 2021 | 1 | 2021 |
| Self-supervised 3d keypoint learning for ego-motion estimation J Tang, RA Ambrus, V Guizilini, S Pillai, H Kim, AD Gaidon US Patent App. 17/093,360, 2021 | 1 | 2021 |
| Depth estimation based on ego-motion estimation and residual flow estimation J Tang, RA Ambrus, V Guizilini, AD Gaidon US Patent App. 18/344,700, 2023 | | 2023 |
| Systems and methods for detecting and matching keypoints between different views of a scene J Tang, RA Ambrus, V Guizilini, S Pillai, H Kim US Patent 11,531,892, 2022 | | 2022 |
| Multi-view depth estimation leveraging offline structure-from-motion J Tang, RA Ambrus, S Pillai, V Guizilini, AD Gaidon US Patent App. 17/368,703, 2022 | | 2022 |
