Meng Tang

Computer Vision and Pattern Recognition · with Boykov · 2013–2018

A researcher in the Science corpus: 4 papers; trusts 331 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #17,460 of 21,575 by trust here, #10,905 by citations
• Artificial Intelligence — 44 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Computational Mechanics — 12 citations into it
• Model Architectures — 8 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Signal Processing — 5 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Meng Tang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yuri Boykov
2. Kaiming He
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Radford M. Neal
7. Ross B. Girshick
8. Jitendra Malik
9. Carsten Rother
10. Yann LeCun

Who trusts Meng Tang overall

1. Ming-Ming Cheng
2. Shiyin Zhang
3. Ning Xu
4. Sudhanshu Mittal
5. Anna Khoreva
6. Hongkai Yu
7. Fabio Cermelli
8. Yi Li
9. Jungbeom Lee
10. Xunlin Zhan

Papers from Meng Tang's perspective

Ordered by how strongly the researchers Meng Tang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• GrabCut in One Cut (2013) — 239 citations
• Normalized Cut Loss for Weakly-Supervised CNN Segmentation (2018) — 346 citations
• On Regularized Losses for Weakly-supervised CNN Segmentation (2018) — 327 citations
• Secrets of GrabCut and Kernel K-Means (2015) — 30 citations