Zequn Jie

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 12 papers; trusts 1,161 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 270 citations into it; #9,650 of 21,575 by trust here, #5,053 by citations
• Artificial Intelligence — 176 citations into it; #29,288 of 35,345 by trust here, #7,471 by citations
• Model Architectures — 43 citations into it
• Reinforcement Learning — 36 citations into it
• Optimization & Training — 32 citations into it
• Aerospace Engineering — 21 citations into it
• Natural Language Processing — 19 citations into it
• Representation & Self-Supervised Learning — 19 citations into it

Whom Zequn Jie trusts overall

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

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

Who trusts Zequn Jie overall

1. Xiaodan Liang
2. Dingwen Zhang
3. Hongyang Xue
4. Scott Fujimoto
5. Giuseppe Vecchio
6. Florin C. Ghesu
7. Zhuangdi Zhu
8. Youngwoon Lee
9. Junwei Han
10. Yuxi Li

Papers from Zequn Jie's perspective

Ordered by how strongly the researchers Zequn Jie 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Reversible Recursive Instance-Level Object Segmentation (2015) — 61 citations
• Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation (2018) — 583 citations
• Temporally Grounding Natural Sentence in Video (2018) — 342 citations
• Central Similarity Quantization for Efficient Image and Video Retrieval (2019) — 374 citations
• Tree-Structured Reinforcement Learning for Sequential Object Localization (2016) — 133 citations
• Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation (2018) — 234 citations
• MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning (2020) — 111 citations
• Anytime Recognition with Routing Convolutional Networks (2019) — 29 citations
• Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations
• A Sufficient Condition for Convergences of Adam and RMSProp (2018) — 462 citations
• Weighted AdaGrad with Unified Momentum (2018) — 43 citations
• Policy Optimization with Demonstrations (2018) — 174 citations