| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 2489 | 2024 |
| Exploring the limits of weakly supervised pretraining D Mahajan, R Girshick, V Ramanathan, K He, M Paluri, Y Li, A Bharambe, ... Proceedings of the European conference on computer vision (ECCV), 181-196, 2018 | 1702 | 2018 |
| Billion-scale semi-supervised learning for image classification IZ Yalniz, H Jégou, K Chen, M Paluri, D Mahajan arXiv preprint arXiv:1905.00546, 2019 | 535 | 2019 |
| Self-supervised learning by cross-modal audio-video clustering H Alwassel, D Mahajan, B Korbar, L Torresani, B Ghanem, D Tran Advances in Neural Information Processing Systems 33, 9758-9770, 2020 | 493 | 2020 |
| Scaling and benchmarking self-supervised visual representation learning P Goyal, D Mahajan, A Gupta, I Misra Proceedings of the ieee/cvf International Conference on computer vision …, 2019 | 493 | 2019 |
| Large-scale weakly-supervised pre-training for video action recognition D Ghadiyaram, D Tran, D Mahajan Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 396 | 2019 |
| From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality Z Ying, H Niu, P Gupta, D Mahajan, D Ghadiyaram, A Bovik Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 371 | 2020 |
| Moving gradients: a path-based method for plausible image interpolation D Mahajan, FC Huang, W Matusik, R Ramamoorthi, P Belhumeur ACM Transactions on Graphics (TOG) 28 (3), 1-11, 2009 | 253 | 2009 |
| Cluster canonical correlation analysis N Rasiwasia, D Mahajan, V Mahadevan, G Aggarwal Artificial intelligence and statistics, 823-831, 2014 | 193 | 2014 |
| Compressive light transport sensing P Peers, DK Mahajan, B Lamond, A Ghosh, W Matusik, R Ramamoorthi, ... ACM Transactions on Graphics (ToG) 28 (1), 1-18, 2009 | 189 | 2009 |
| Gradient boosted decision trees for high dimensional sparse output S Si, H Zhang, SS Keerthi, D Mahajan, IS Dhillon, CJ Hsieh International conference on machine learning, 3182-3190, 2017 | 178 | 2017 |
| What makes a video a video: Analyzing temporal information in video understanding models and datasets DA Huang, V Ramanathan, D Mahajan, L Torresani, M Paluri, L Fei-Fei, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 168 | 2018 |
| Clusterfit: Improving generalization of visual representations X Yan, I Misra, A Gupta, D Ghadiyaram, D Mahajan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 165 | 2020 |
| Learning hierarchical similarity metrics N Verma, D Mahajan, S Sellamanickam, V Nair 2012 IEEE conference on computer vision and pattern recognition, 2280-2287, 2012 | 161 | 2012 |
| Emu: Enhancing image generation models using photogenic needles in a haystack X Dai, J Hou, CY Ma, S Tsai, J Wang, R Wang, P Zhang, S Vandenhende, ... arXiv preprint arXiv:2309.15807, 2023 | 160 | 2023 |
| First steps toward an electronic field guide for plants G Agarwal, P Belhumeur, S Feiner, D Jacobs, WJ Kress, R Ramamoorthi, ... Taxon 55 (3), 597-610, 2006 | 142 | 2006 |
| Don't judge an object by its context: Learning to overcome contextual bias KK Singh, D Mahajan, K Grauman, YJ Lee, M Feiszli, D Ghadiyaram Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 137 | 2020 |
| A first-order analysis of lighting, shading, and shadows R Ramamoorthi, D Mahajan, P Belhumeur ACM Transactions on Graphics (TOG) 26 (1), 2-es, 2007 | 116 | 2007 |
| A joint learning framework for attribute models and object descriptions D Mahajan, S Sellamanickam, V Nair 2011 International Conference on Computer Vision, 1227-1234, 2011 | 104 | 2011 |
| The llama 3 herd of models, 2024 A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... URL https://arxiv. org/abs/2407.21783 2407, 21783, 0 | 102 | |
Ravi RamamoorthiProfessor of Computer Science, University of California, San Diegocs.ucsd.edu의 이메일 확인됨
