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FroDO: From Detections to 3D Objects

May 11, 2020
Kejie Li, Martin Rünz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe

* To be published in CVPR 2020. The first two authors contributed equally 

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Multiphase flow prediction with deep neural networks

Oct 21, 2019
Gege Wen, Meng Tang, Sally M. Benson


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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Aug 16, 2019
Meng Tang, Yimin Liu, Louis J. Durlofsky


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Learning Compressed Sentence Representations for On-Device Text Processing

Jun 19, 2019
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz, Lawrence Carin

* To appear at ACL 2019 

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ADM for grid CRF loss in CNN segmentation

Sep 07, 2018
Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov


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Constrained-CNN losses forweakly supervised segmentation

May 12, 2018
Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed

* Submitted to the 1st conference on Medical Image with Deep Learning (MIDL) 

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On Regularized Losses for Weakly-supervised CNN Segmentation

Apr 10, 2018
Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov


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Normalized Cut Loss for Weakly-supervised CNN Segmentation

Apr 04, 2018
Meng Tang, Abdelaziz Djelouah, Federico Perazzi, Yuri Boykov, Christopher Schroers

* Accepted at CVPR 2018 

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Kernel clustering: density biases and solutions

Dec 06, 2017
Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov


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Kernel Cuts: MRF meets Kernel & Spectral Clustering

Sep 21, 2016
Meng Tang, Dmitrii Marin, Ismail Ben Ayed, Yuri Boykov

* The main ideas of this work are published in our conference papers: "Normalized cut meets MRF" [70] (ECCV 2016) and "Secrets of Grabcut and kernel K-means" [41] (ICCV 2015) 

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