Sheng Tang

Computer Vision and Pattern Recognition · 2009–2020

A researcher in the Science corpus: 8 papers; trusts 926 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 204 citations into it; #16,206 of 21,575 by trust here, #6,371 by citations
• Artificial Intelligence — 136 citations into it
• Model Architectures — 27 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Optimization & Training — 21 citations into it
• Efficiency & Compression — 16 citations into it
• Multimodal Learning — 13 citations into it

Whom Sheng Tang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Diederik P. Kingma
7. Yann LeCun
8. Shaoqing Ren
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Sheng Tang overall

1. Kailun Yang
2. Yuhui Yuan
3. Huayao Liu
4. Abdullah Rashwan

Papers from Sheng Tang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax (2020) — 306 citations
• The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation (2020) — 207 citations
• The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation (2020) — 7 citations
• Scale-Adaptive Convolutions for Scene Parsing (2017) — 196 citations
• CGNet: A Light-Weight Context Guided Network for Semantic Segmentation (2018) — 786 citations
• Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks (2018) — 127 citations
• Visual Relation Grounding in Videos (2020) — 45 citations
• A density-based method for adaptive LDA model selection (2009) — 826 citations