| Compact bilinear pooling Y Gao, O Beijbom, N Zhang, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 531 | 2016 |
| nuscenes: A multimodal dataset for autonomous driving H Caesar, V Bankiti, AH Lang, S Vora, VE Liong, Q Xu, A Krishnan, Y Pan, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 488 | 2020 |
| Pointpillars: Fast encoders for object detection from point clouds AH Lang, S Vora, H Caesar, L Zhou, J Yang, O Beijbom Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 373 | 2019 |
| Automated annotation of coral reef survey images O Beijbom, PJ Edmunds, DI Kline, BG Mitchell, D Kriegman Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on …, 2012 | 197 | 2012 |
| Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation O Beijbom, PJ Edmunds, C Roelfsema, J Smith, DI Kline, BP Neal, ... PloS one 10 (7), e0130312, 2015 | 147 | 2015 |
| Menu-match: Restaurant-specific food logging from images O Beijbom, N Joshi, D Morris, S Saponas, S Khullar 2015 IEEE Winter Conference on Applications of Computer Vision, 844-851, 2015 | 103 | 2015 |
| Best Practices for Fine-tuning Visual Classifiers to New Domains B Chu, V Madhavan, O Beijbom, J Hoffman, T Darrell European Conference on Computer Vision, workshop on TASK-CV, 2016 | 81 | 2016 |
| The Catlin Seaview Survey–kilometre‐scale seascape assessment, and monitoring of coral reef ecosystems M González‐Rivero, P Bongaerts, O Beijbom, O Pizarro, A Friedman, ... Aquatic Conservation: Marine and Freshwater Ecosystems 24 (S2), 184-198, 2014 | 56 | 2014 |
| Efficient large-scale structured learning S Branson, O Beijbom, S Belongie Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 50 | 2013 |
| Whoi-plankton-a large scale fine grained visual recognition benchmark dataset for plankton classification EC Orenstein, O Beijbom, EE Peacock, HM Sosik arXiv preprint arXiv:1510.00745, 2015 | 46 | 2015 |
| Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis M González-Rivero, O Beijbom, A Rodriguez-Ramirez, T Holtrop, ... Remote Sensing 8 (1), 30, 2016 | 43 | 2016 |
| Domain Adaptations for Computer Vision Applications O Beijbom Tech Report, University of California San Diego, 2012 | 42 | 2012 |
| Improving automated annotation of benthic survey images using wide-band fluorescence O Beijbom, T Treibitz, DI Kline, G Eyal, A Khen, B Neal, Y Loya, ... Scientific reports 6 (1), 1-11, 2016 | 38 | 2016 |
| Transfer learning and deep feature extraction for planktonic image data sets EC Orenstein, O Beijbom 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 1082-1088, 2017 | 33 | 2017 |
| Covernet: Multimodal behavior prediction using trajectory sets T Phan-Minh, EC Grigore, FA Boulton, O Beijbom, EM Wolff Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 31 | 2020 |
| Wide field-of-view fluorescence imaging of coral reefs T Treibitz, BP Neal, DI Kline, O Beijbom, PLD Roberts, BG Mitchell, ... Scientific reports 5 (1), 1-9, 2015 | 31 | 2015 |
| Pointpainting: Sequential fusion for 3d object detection S Vora, AH Lang, B Helou, O Beijbom Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 28 | 2020 |
| Food logging from images NS Joshi, S Khullar, TS Saponas, D Morris, O Beijbom US Patent 9,977,980, 2018 | 24 | 2018 |
| Guess-averse loss functions for cost-sensitive multiclass boosting O Beijbom, M Saberian, D Kriegman, N Vasconcelos International Conference on Machine Learning, 586-594, 2014 | 23 | 2014 |
| Leveraging automated image analysis tools to transform our capacity to assess status and trends of coral reefs ID Williams, CS Couch, O Beijbom, TA Oliver, B Vargas-Angel, ... Frontiers in Marine Science 6, 222, 2019 | 21 | 2019 |
David KriegmanProfessor of Computer Science, University of California, San DiegoVerified email at eng.ucsd.edu
