Aggregated residual transformations for deep neural networks S Xie, R Girshick, P Dollár, Z Tu, K He Computer Vision and Pattern Recognition, 2017. CVPR 2017. IEEE Conference on, 2017 | 12660 | 2017 |
Holistically-nested edge detection S Xie, Z Tu Proceedings of the IEEE international conference on computer vision, 1395-1403, 2015 | 4146 | 2015 |
Deeply-Supervised Nets CY Lee*, S Xie*, PW Gallagher, Z Zhang, Z Tu AISTATS 2 (3), 5, 2015 | 2803 | 2015 |
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification S Xie, C Sun, J Huang, Z Tu, K Murphy Proceedings of the European conference on computer vision (ECCV), 305-321, 2018 | 1794* | 2018 |
Similarity network fusion for aggregating data types on a genomic scale B Wang, AM Mezlini, F Demir, M Fiume, Z Tu, M Brudno, B Haibe-Kains, ... Nature methods 11 (3), 333-337, 2014 | 1779 | 2014 |
Deeply supervised salient object detection with short connections Q Hou, MM Cheng, X Hu, A Borji, Z Tu, PHS Torr Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1736 | 2017 |
Integral channel features P Dollár, Z Tu, P Perona, S Belongie BMVC Press, 2009 | 1678 | 2009 |
Detecting texts of arbitrary orientations in natural images C Yao, X Bai, W Liu, Y Ma, Z Tu 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1083-1090, 2012 | 1051 | 2012 |
Auto-context and its application to high-level vision tasks Z Tu Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on …, 2008 | 1002* | 2008 |
Image parsing: Unifying segmentation, detection, and recognition Z Tu, X Chen, A Yuille, SC Zhu Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on …, 2003 | 910 | 2003 |
Image segmentation by data-driven Markov chain Monte Carlo Z Tu, SC Zhu IEEE Transactions on pattern analysis and machine intelligence 24 (5), 657-673, 2002 | 895 | 2002 |
Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree CY Lee, PW Gallagher, Z Tu International Conference on Artificial Intelligence and Statistics, 2016 | 795 | 2016 |
Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering Z Tu Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on 2 …, 2005 | 684 | 2005 |
Robust brain extraction across datasets and comparison with publicly available methods JE Iglesias, CY Liu, PM Thompson, Z Tu IEEE transactions on medical imaging 30 (9), 1617-1634, 2011 | 665 | 2011 |
Supervised learning of edges and object boundaries P Dollar, Z Tu, S Belongie 2006 IEEE computer society conference on computer vision and pattern …, 2006 | 614 | 2006 |
Robust point matching via vector field consensus J Ma, J Zhao, J Tian, AL Yuille, Z Tu IEEE Transactions on Image Processing 23 (4), 1706-1721, 2014 | 572 | 2014 |
Weakly supervised histopathology cancer image segmentation and classification Y Xu, JY Zhu, I Eric, C Chang, M Lai, Z Tu Medical image analysis 18 (3), 591-604, 2014 | 490* | 2014 |
Cluster-based co-saliency detection H Fu, X Cao, Z Tu IEEE Transactions on Image Processing 22 (10), 3766-3778, 2013 | 468 | 2013 |
Attentional shapecontextnet for point cloud recognition S Xie, S Liu, Z Chen, Z Tu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 382 | 2018 |
Learning context-sensitive shape similarity by graph transduction X Bai, X Yang, LJ Latecki, W Liu, Z Tu IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (5), 861-874, 2010 | 358 | 2010 |