| Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic data M Danielczuk, M Matl, S Gupta, A Li, A Lee, J Mahler, K Goldberg 2019 International Conference on Robotics and Automation (ICRA), 7283-7290, 2019 | 208 | 2019 |
| Searching for fast model families on datacenter accelerators S Li, M Tan, R Pang, A Li, L Cheng, QV Le, NP Jouppi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 33 | 2021 |
| Segmenting unknown 3d objects from real depth images using mask r-cnn trained on synthetic point clouds M Danielczuk, M Matl, S Gupta, A Li, A Lee, J Mahler, K Goldberg arXiv preprint arXiv:1809.05825 16, 2018 | 26 | 2018 |
| Neural Architecture Scaling For Hardware Accelerators A Li, S Li, M Tan, R Pang, L Cheng, QV Le, NP Jouppi US Patent App. 17/175,029, 2022 | 9 | 2022 |
| One-shot shape-based amodal-to-modal instance segmentation A Li, M Danielczuk, K Goldberg 2020 IEEE 16th International Conference on Automation Science and …, 2020 | 4 | 2020 |
| Hardware-optimized neural architecture search S Li, NP Jouppi, QV Le, M Tan, R Pang, L Cheng, A Li US Patent App. 17/039,178, 2022 | 1 | 2022 |
| Searching for Fast Models on Datacenter Accelerators S Li, M Tan, R Pang, A Li, QV Le, N Jouppi | | 2021 |
| Towards Generalization of One-Shot Amodal-To-Modal Instance Segmentation Using Shape Masks A Li | | 2020 |
| Supplementary Material for “Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data” M Danielczuk, M Matl, S Gupta, A Li, A Lee, J Mahler, K Goldberg | | |
