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Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2155 | 2023 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 676 | 2024 |
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset S Ettinger, S Cheng, B Caine, C Liu, H Zhao, S Pradhan, Y Chai, B Sapp, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 534 | 2021 |
Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection Y Li, AW Yu, T Meng, B Caine, J Ngiam, D Peng, J Shen, Y Lu, D Zhou, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 391 | 2022 |
Scene transformer: A unified architecture for predicting multiple agent trajectories J Ngiam, B Caine, V Vasudevan, Z Zhang, HTL Chiang, J Ling, R Roelofs, ... arXiv preprint arXiv:2106.08417, 2021 | 270 | 2021 |
Starnet: Targeted computation for object detection in point clouds J Ngiam, B Caine, W Han, B Yang, Y Chai, P Sun, Y Zhou, X Yi, O Alsharif, ... arXiv preprint arXiv:1908.11069, 2019 | 131 | 2019 |
Capabilities of gemini models in medicine K Saab, T Tu, WH Weng, R Tanno, D Stutz, E Wulczyn, F Zhang, ... arXiv preprint arXiv:2404.18416, 2024 | 95 | 2024 |
Improving 3d object detection through progressive population based augmentation S Cheng, Z Leng, ED Cubuk, B Zoph, C Bai, J Ngiam, Y Song, B Caine, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 94 | 2020 |
To the point: Efficient 3d object detection in the range image with graph convolution kernels Y Chai, P Sun, J Ngiam, W Wang, B Caine, V Vasudevan, X Zhang, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 85 | 2021 |
Scene transformer: A unified multi-task model for behavior prediction and planning J Ngiam, B Caine, V Vasudevan, Z Zhang, HTL Chiang, J Ling, R Roelofs, ... arXiv preprint arXiv:2106.08417 2 (7), 2021 | 79 | 2021 |
When does dough become a bagel? analyzing the remaining mistakes on imagenet V Vasudevan, B Caine, R Gontijo Lopes, S Fridovich-Keil, R Roelofs Advances in Neural Information Processing Systems 35, 6720-6734, 2022 | 52 | 2022 |
Pseudo-labeling for scalable 3d object detection B Caine, R Roelofs, V Vasudevan, J Ngiam, Y Chai, Z Chen, J Shlens arXiv preprint arXiv:2103.02093, 2021 | 45 | 2021 |
Mammut: A simple architecture for joint learning for multimodal tasks W Kuo, AJ Piergiovanni, D Kim, X Luo, B Caine, W Li, A Ogale, L Zhou, ... arXiv preprint arXiv:2303.16839, 2023 | 28 | 2023 |
Streaming object detection for 3-d point clouds W Han, Z Zhang, B Caine, B Yang, C Sprunk, O Alsharif, J Ngiam, ... European Conference on Computer Vision, 423-441, 2020 | 27 | 2020 |
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Pseudoaugment: Learning to use unlabeled data for data augmentation in point clouds Z Leng, S Cheng, B Caine, W Wang, X Zhang, J Shlens, M Tan, ... European conference on computer vision, 555-572, 2022 | 17 | 2022 |
Causalagents: A robustness benchmark for motion forecasting using causal relationships R Roelofs, L Sun, B Caine, KS Refaat, B Sapp, S Ettinger, W Chai arXiv preprint arXiv:2207.03586, 2022 | 8 | 2022 |
Imagen 3 J Baldridge, J Bauer, M Bhutani, N Brichtova, A Bunner, K Chan, Y Chen, ... arXiv preprint arXiv:2408.07009, 2024 | 7 | 2024 |
Object detection in point clouds J Shlens, PAP Nguyen, BJ Caine, J Ngiam, W Han, BC Yang, Y Chai, ... US Patent 11,450,120, 2022 | 2 | 2022 |