Meng Tang

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Western University

FroDO: From Detections to 3D Objects

Object-oriented maps are important for scene understanding since they jo...
05/11/2020 ∙ by Kejie Li, et al. ∙ 0 ∙ share

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Multiphase flow prediction with deep neural networks

This paper proposes a deep neural network approach for predicting multip...
10/21/2019 ∙ by Gege Wen, et al. ∙ 22 ∙ share

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

A deep-learning-based surrogate model is developed and applied for predi...
08/16/2019 ∙ by Meng Tang, et al. ∙ 3 ∙ share

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Learning Compressed Sentence Representations for On-Device Text Processing

Vector representations of sentences, trained on massive text corpora, ar...
06/19/2019 ∙ by Dinghan Shen, et al. ∙ 0 ∙ share

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ADM for grid CRF loss in CNN segmentation

Variants of gradient descent (GD) dominate CNN loss minimization in comp...
09/07/2018 ∙ by Dmitrii Marin, et al. ∙ 0 ∙ share

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Constrained-CNN losses forweakly supervised segmentation

Weak supervision, e.g., in the form of partial labels or image tags, is ...
05/12/2018 ∙ by Hoel Kervadec, et al. ∙ 0 ∙ share

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Normalized Cut Loss for Weakly-supervised CNN Segmentation

Most recent semantic segmentation methods train deep convolutional neura...
04/04/2018 ∙ by Meng Tang, et al. ∙ 2 ∙ share

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On Regularized Losses for Weakly-supervised CNN Segmentation

Minimization of regularized losses is a principled approach to weak supe...
03/26/2018 ∙ by Meng Tang, et al. ∙ 0 ∙ share

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Kernel clustering: density biases and solutions

Kernel methods are popular in clustering due to their generality and dis...
05/16/2017 ∙ by Dmitrii Marin, et al. ∙ 0 ∙ share

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Kernel Cuts: MRF meets Kernel & Spectral Clustering

We propose a new segmentation model combining common regularization ener...
06/24/2015 ∙ by Meng Tang, et al. ∙ 0 ∙ share

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Meng Tang

Featured Co-authors

Lawrence Carin

Ian Reid

Ismail Ben Ayed

Jose Dolz

Dinghan Shen

Eric Granger

Asli Celikyilmaz

Yuri Boykov

Federico Perazzi

Qian Yang

Lourdes Agapito

FroDO: From Detections to 3D Objects

Multiphase flow prediction with deep neural networks

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

Learning Compressed Sentence Representations for On-Device Text Processing

ADM for grid CRF loss in CNN segmentation

Constrained-CNN losses forweakly supervised segmentation

Normalized Cut Loss for Weakly-supervised CNN Segmentation

On Regularized Losses for Weakly-supervised CNN Segmentation

Kernel clustering: density biases and solutions

Kernel Cuts: MRF meets Kernel & Spectral Clustering