Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Tao Wang

Tao Wang

Half-Truth: A Partially Fake Audio Detection Dataset


Apr 08, 2021
Jiangyan Yi, Ye Bai, Jianhua Tao, Zhengkun Tian, Chenglong Wang, Tao Wang, Ruibo Fu

* submitted to Interspeech 2021 
  Access Paper or Ask Questions

IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction


Apr 03, 2021
Tao Wang, Wenjun Xia, Zexin Lu, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

* 10 pages, 8 figures 
  Access Paper or Ask Questions

Auto Correcting in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation


Mar 30, 2021
Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei Li, Deyi Xiong

Figure 1 for Auto Correcting in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Figure 2 for Auto Correcting in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Figure 3 for Auto Correcting in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Figure 4 for Auto Correcting in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
* 8 pages, 3 figures, 7 tables 
  Access Paper or Ask Questions

A Universal Model for Cross Modality Mapping by Relational Reasoning


Feb 26, 2021
Zun Li, Congyan Lang, Liqian Liang, Tao Wang, Songhe Feng, Jun Wu, Yidong Li

Figure 1 for A Universal Model for Cross Modality Mapping by Relational Reasoning
Figure 2 for A Universal Model for Cross Modality Mapping by Relational Reasoning
Figure 3 for A Universal Model for Cross Modality Mapping by Relational Reasoning
Figure 4 for A Universal Model for Cross Modality Mapping by Relational Reasoning
* 10 pages, 4 figures 
  Access Paper or Ask Questions

Attention Models for Point Clouds in Deep Learning: A Survey


Feb 22, 2021
Xu Wang, Yi Jin, Yigang Cen, Tao Wang, Yidong Li


  Access Paper or Ask Questions

DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction


Feb 16, 2021
Tao Wang, Wenjun Xia, Yongqiang Huang, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

Figure 1 for DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Figure 2 for DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Figure 3 for DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Figure 4 for DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
* 29 pages, 11 figures 
  Access Paper or Ask Questions

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet


Jan 28, 2021
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, Shuicheng Yan

Figure 1 for Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Figure 2 for Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Figure 3 for Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Figure 4 for Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
* codes: https://github.com/yitu-opensource/T2T-ViT 
  Access Paper or Ask Questions

An Investigation of Potential Function Designs for Neural CRF


Nov 11, 2020
Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for An Investigation of Potential Function Designs for Neural CRF
Figure 2 for An Investigation of Potential Function Designs for Neural CRF
Figure 3 for An Investigation of Potential Function Designs for Neural CRF
Figure 4 for An Investigation of Potential Function Designs for Neural CRF

  Access Paper or Ask Questions

Exploring the limits of Concurrency in ML Training on Google TPUs


Nov 07, 2020
Sameer Kumar, James Bradbury, Cliff Young, Yu Emma Wang, Anselm Levskaya, Blake Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing

Figure 1 for Exploring the limits of Concurrency in ML Training on Google TPUs
Figure 2 for Exploring the limits of Concurrency in ML Training on Google TPUs
Figure 3 for Exploring the limits of Concurrency in ML Training on Google TPUs
Figure 4 for Exploring the limits of Concurrency in ML Training on Google TPUs

  Access Paper or Ask Questions

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising


Oct 30, 2020
Tengfei Liang, Yi Jin, Yidong Li, Tao Wang, Songhe Feng, Congyan Lang

Figure 1 for EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Figure 2 for EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Figure 3 for EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Figure 4 for EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
* 8 pages, 7 figures, 3 tables 
  Access Paper or Ask Questions

Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes


Oct 16, 2020
Li Yuan, Yichen Zhou, Shuning Chang, Ziyuan Huang, Yunpeng Chen, Xuecheng Nie, Tao Wang, Jiashi Feng, Shuicheng Yan

Figure 1 for Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Figure 2 for Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Figure 3 for Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Figure 4 for Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
* ACM Multimedia 2020 
 * 1'st Place in ACM Multimedia Grand Challenge: Human in Events, Track4: Person-level Action Recognition in Complex Events 
  Access Paper or Ask Questions

Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation


Oct 14, 2020
HanQin Cai, Keaton Hamm, Longxiu Huang, Jiaqi Li, Tao Wang

Figure 1 for Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Figure 2 for Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Figure 3 for Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Figure 4 for Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation

  Access Paper or Ask Questions

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network


Oct 12, 2020
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 2 for AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 3 for AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 4 for AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
* Accept to Main Conference of EMNLP 2020 (Short). Camera-ready, 8 Pages 
  Access Paper or Ask Questions

Structural Knowledge Distillation


Oct 10, 2020
Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for Structural Knowledge Distillation
Figure 2 for Structural Knowledge Distillation
Figure 3 for Structural Knowledge Distillation
Figure 4 for Structural Knowledge Distillation
* Under review as a conference paper of ICLR 2021. 15 pages 
  Access Paper or Ask Questions

Automated Concatenation of Embeddings for Structured Prediction


Oct 10, 2020
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for Automated Concatenation of Embeddings for Structured Prediction
Figure 2 for Automated Concatenation of Embeddings for Structured Prediction
Figure 3 for Automated Concatenation of Embeddings for Structured Prediction
Figure 4 for Automated Concatenation of Embeddings for Structured Prediction
* We propose ACE, which achieves new SOTA for 6 NLP tasks over 23 datasets. Under review as a conference paper at ICLR 2021. 19 pages 
  Access Paper or Ask Questions

More Embeddings, Better Sequence Labelers?


Oct 10, 2020
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for More Embeddings, Better Sequence Labelers?
Figure 2 for More Embeddings, Better Sequence Labelers?
Figure 3 for More Embeddings, Better Sequence Labelers?
Figure 4 for More Embeddings, Better Sequence Labelers?
* Accepted to Findings of EMNLP 2020. Camera-ready, 16 pages 
  Access Paper or Ask Questions

Fast and Accurate Sequence Labeling with Approximate Inference Network


Sep 17, 2020
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Figure 1 for Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 2 for Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 3 for Fast and Accurate Sequence Labeling with Approximate Inference Network
Figure 4 for Fast and Accurate Sequence Labeling with Approximate Inference Network
* Accept to Main Conference of EMNLP 2020 (Short). 8 Pages 
  Access Paper or Ask Questions

Finding Action Tubes with a Sparse-to-Dense Framework


Aug 30, 2020
Yuxi Li, Weiyao Lin, Tao Wang, John See, Rui Qian, Ning Xu, Limin Wang, Shugong Xu

Figure 1 for Finding Action Tubes with a Sparse-to-Dense Framework
Figure 2 for Finding Action Tubes with a Sparse-to-Dense Framework
Figure 3 for Finding Action Tubes with a Sparse-to-Dense Framework
Figure 4 for Finding Action Tubes with a Sparse-to-Dense Framework
* 5 figures; AAAI 2020 
  Access Paper or Ask Questions

The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation


Jul 31, 2020
Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Junhao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

Figure 1 for The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation
Figure 2 for The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation
Figure 3 for The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation
Figure 4 for The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation
* LVIS 2019 challenge winner, performance significantly improved after challenge submission, accepted at ECCV 2019 
  Access Paper or Ask Questions

A Multi-Level Approach to Waste Object Segmentation


Jul 08, 2020
Tao Wang, Yuanzheng Cai, Lingyu Liang, Dongyi Ye

Figure 1 for A Multi-Level Approach to Waste Object Segmentation
Figure 2 for A Multi-Level Approach to Waste Object Segmentation
Figure 3 for A Multi-Level Approach to Waste Object Segmentation
Figure 4 for A Multi-Level Approach to Waste Object Segmentation
* Paper appears in Sensors 2020, 20(14), 3816 
  Access Paper or Ask Questions

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax


Jun 18, 2020
Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng

Figure 1 for Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Figure 2 for Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Figure 3 for Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Figure 4 for Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
* CVPR 2020 (Oral). Code is available at https://github.com/FishYuLi/BalancedGroupSoftmax 
  Access Paper or Ask Questions

Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events


May 19, 2020
Weiyao Lin, Huabin Liu, Shizhan Liu, Yuxi Li, Guo-Jun Qi, Rui Qian, Tao Wang, Nicu Sebe, Ning Xu, Hongkai Xiong, Mubarak Shah

Figure 1 for Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
Figure 2 for Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
Figure 3 for Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
Figure 4 for Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
* Dataset for ACM MM'20 Grand Challenge on Large-scale Human-centric Video Analysis in Complex Events (http://humaninevents.org) 
  Access Paper or Ask Questions

Making Robots Draw A Vivid Portrait In Two Minutes


May 12, 2020
Fei Gao, Jingjie Zhu, Zeyuan Yu, Peng Li, Tao Wang

Figure 1 for Making Robots Draw A Vivid Portrait In Two Minutes
Figure 2 for Making Robots Draw A Vivid Portrait In Two Minutes
Figure 3 for Making Robots Draw A Vivid Portrait In Two Minutes
Figure 4 for Making Robots Draw A Vivid Portrait In Two Minutes
* 7 pages, 7 figures 
  Access Paper or Ask Questions

Structure-Level Knowledge Distillation For Multilingual Sequence Labeling


Apr 29, 2020
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

Figure 1 for Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Figure 2 for Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Figure 3 for Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Figure 4 for Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
* Accepted to ACL 2020, camera-ready. 14 pages 
  Access Paper or Ask Questions

Automatic low-bit hybrid quantization of neural networks through meta learning


Apr 24, 2020
Tao Wang, Junsong Wang, Chang Xu, Chao Xue

Figure 1 for Automatic low-bit hybrid quantization of neural networks through meta learning
Figure 2 for Automatic low-bit hybrid quantization of neural networks through meta learning
Figure 3 for Automatic low-bit hybrid quantization of neural networks through meta learning
Figure 4 for Automatic low-bit hybrid quantization of neural networks through meta learning
* 7 pages, 3 figures 
  Access Paper or Ask Questions