| Mobilenets: Efficient convolutional neural networks for mobile vision applications AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861, 2017 | 7034 | 2017 |
| Mobilenetv2: Inverted residuals and linear bottlenecks M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 3756 | 2018 |
| Speed/accuracy trade-offs for modern convolutional object detectors J Huang, V Rathod, C Sun, M Zhu, A Korattikara, A Fathi, I Fischer, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1805 | 2017 |
| Quantization and training of neural networks for efficient integer-arithmetic-only inference B Jacob, S Kligys, B Chen, M Zhu, M Tang, A Howard, H Adam, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 695 | 2018 |
| Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation A Howard, A Zhmoginov, LC Chen, M Sandler, M Zhu | 428 | 2018 |
| Sparseness meets deepness: 3d human pose estimation from monocular video X Zhou, M Zhu, S Leonardos, KG Derpanis, K Daniilidis Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 358 | 2016 |
| From Actemes to Action: A Strongly-supervised Representation for Detailed Action Understanding W Zhang, M Zhu, KG Derpanis International Conference on Computer Vision, 2013 | 194 | 2013 |
| Sparse representation for 3D shape estimation: A convex relaxation approach X Zhou, M Zhu, S Leonardos, K Daniilidis IEEE transactions on pattern analysis and machine intelligence 39 (8), 1648-1661, 2016 | 118 | 2016 |
| Monocap: Monocular human motion capture using a cnn coupled with a geometric prior X Zhou, M Zhu, G Pavlakos, S Leonardos, KG Derpanis, K Daniilidis IEEE transactions on pattern analysis and machine intelligence 41 (4), 901-914, 2018 | 111 | 2018 |
| Single Image 3D Object Detection and Pose Estimation for Grasping M Zhu, KG Derpanis, Y Yang, S Brahmbhatt, M Zhang, C Phillips, M Lecce, ... International Conference on Robotics and Automation, 2014 | 108 | 2014 |
| Multi-image matching via fast alternating minimization X Zhou, M Zhu, K Daniilidis Proceedings of the IEEE International Conference on Computer Vision, 4032-4040, 2015 | 99 | 2015 |
| Mobile video object detection with temporally-aware feature maps M Liu, M Zhu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 79 | 2018 |
| Automatic spatially-aware fashion concept discovery X Han, Z Wu, PX Huang, X Zhang, M Zhu, Y Li, Y Zhao, LS Davis Proceedings of the IEEE International Conference on Computer Vision, 1463-1471, 2017 | 71 | 2017 |
| & Adam, H.(2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand arXiv preprint arXiv:1704.04861, 0 | 62 | |
| Semantic Localization Via the Matrix Permanent N Atanasov, M Zhu, K Daniilidis, GJ Pappas Robotics: Science and Systems, 2014 | 61 | 2014 |
| Localization from semantic observations via the matrix permanent N Atanasov, M Zhu, K Daniilidis, GJ Pappas The International Journal of Robotics Research 35 (1-3), 73-99, 2016 | 45 | 2016 |
| Tensorflow object detection api J Huang, V Rathod, D Chow, C Sun, M Zhu, A Fathi, Z Lu Code: github. com/tensorflow/models/tree/master/object detection, 2017 | 27 | 2017 |
| Inverted residuals and linear bottlenecks: mobile networks for classification M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Detection and Segmentation, 2018 | 24 | 2018 |
| Active deformable part models inference M Zhu, N Atanasov, GJ Pappas, K Daniilidis European Conference on Computer Vision, 281-296, 2014 | 22 | 2014 |
| Single image pop-up from discriminatively learned parts M Zhu, X Zhou, K Daniilidis Proceedings of the IEEE International Conference on Computer Vision, 927-935, 2015 | 21 | 2015 |
Andrew HowardGoogle AIVerified email at google.com
