| Mobilenets: Efficient convolutional neural networks for mobile vision applications AG Howard arXiv preprint arXiv:1704.04861, 2017 | 28301 | 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 | 26060 | 2018 |
| 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 | 3819 | 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 | 3586 | 2017 |
| Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation A Howard, A Zhmoginov, LC Chen, M Sandler, M Zhu Proc. CVPR, 4510-4520, 2018 | 1208 | 2018 |
| MobileNets: efficient convolutional neural networks for mobile vision applications (2017) AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861 126, 2017 | 655 | 2017 |
| 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 | 531 | 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 | 439 | 2013 |
| 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 | 284 | 2017 |
| 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 | 278 | 2014 |
| Mobile video object detection with temporally-aware feature maps M Liu, M Zhu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 248 | 2018 |
| 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 | 246 | 2018 |
| MobileNetV2: Inverted residuals and linear bottlenecks. arXiv 2018 M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen arXiv preprint arXiv:1801.04381, 1801 | 232 | 1801 |
| Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint (2017) AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861, 2017 | 222 | 2017 |
| Proceedings of the IEEE conference on computer vision and pattern recognition M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 198 | 2018 |
| 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 | 182 | 2016 |
| 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 | 172 | 2015 |
| Detect-to-retrieve: Efficient regional aggregation for image search M Teichmann, A Araujo, M Zhu, J Sim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 150 | 2019 |
| Looking fast and slow: Memory-guided mobile video object detection M Liu, M Zhu, M White, Y Li, D Kalenichenko arXiv preprint arXiv:1903.10172, 2019 | 109 | 2019 |
| Mobilenets: E cient convolutional neural networks for mobile vision applications AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861 2, 2017 | 93 | 2017 |
Andrew HowardGoogle Research在 google.com 的电子邮件经过验证
