Zhiqiang Shen

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 15 papers; trusts 1,784 researchers and is trusted by 229 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 449 citations into it; #2,571 of 21,575 by trust here, #2,130 by citations
• Artificial Intelligence — 320 citations into it; #13,244 of 35,345 by trust here, #6,330 by citations
• Efficiency & Compression — 107 citations into it; #418 of 1,264 by trust here, #254 by citations
• Model Architectures — 80 citations into it
• Representation & Self-Supervised Learning — 69 citations into it
• Optimization & Training — 63 citations into it; #1,218 of 2,601 by trust here, #1,195 by citations
• Evaluation & Benchmarks — 46 citations into it
• Signal Processing — 18 citations into it

Whom Zhiqiang Shen trusts overall

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

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

Who trusts Zhiqiang Shen overall

1. Jiahui Yu
2. Zechun Liu
3. Fartash Faghri
4. Changlin Li
5. Marios Savvides
6. Eric P. Xing
7. Thomas S. Huang
8. Zhuang Liu
9. Tribhuvanesh Orekondy
10. Jianping Gou

Papers from Zhiqiang Shen's perspective

Ordered by how strongly the researchers Zhiqiang Shen 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Learning Efficient Convolutional Networks through Network Slimming (2017) — 2,811 citations
• DSOD: Learning Deeply Supervised Object Detectors from Scratch (2017) — 628 citations
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (2021) — 24 citations
• MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks (2020) — 69 citations
• Multiple Granularity Descriptors for Fine-Grained Categorization (2015) — 223 citations
• Soft Anchor-Point Object Detection (2019) — 162 citations
• A Fast Knowledge Distillation Framework for Visual Recognition (2021) — 68 citations
• Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation (2021) — 167 citations
• Solving Missing-Annotation Object Detection with Background Recalibration Loss (2020) — 61 citations
• Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study (2021) — 83 citations
• ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions (2020) — 434 citations
• Online Ensemble Model Compression Using Knowledge Distillation (2020) — 59 citations
• Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning (2020) — 122 citations
• MEAL: Multi-Model Ensemble via Adversarial Learning (2018) — 157 citations
• Do More Dropouts in Pool5 Feature Maps for Better Object Detection (2014) — 5 citations