Wenjun Zeng

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 14 papers; trusts 1,457 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 490 citations into it; #3,820 of 21,575 by trust here, #2,322 by citations
• Artificial Intelligence — 193 citations into it; #8,960 of 35,345 by trust here, #7,062 by citations
• Biomedical Engineering — 119 citations into it; #342 of 10,734 by trust here, #1,237 by citations
• Model Architectures — 81 citations into it; #797 of 2,278 by trust here, #827 by citations
• Human-Computer Interaction — 59 citations into it; #840 of 2,253 by trust here, #503 by citations
• Signal Processing — 34 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Optimization & Training — 33 citations into it

Whom Wenjun Zeng trusts overall

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

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

Who trusts Wenjun Zeng overall

1. Jun Liu
2. Pengfei Zhang
3. Pichao Wang
4. Yifan Zhang
5. C. Li
6. Amir Shahroudy
7. Cuiling Lan
8. Yisheng Song
9. Chenyang Si
10. Qiuhong Ke

Papers from Wenjun Zeng's perspective

Ordered by how strongly the researchers Wenjun Zeng 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Twofold Siamese Network for Real-Time Object Tracking (2018) — 608 citations
• An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (2016) — 1,062 citations
• FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking (2020) — 1,833 citations
• Benchmarking Single-Image Dehazing and Beyond (2017) — 2,219 citations
• Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks (2016) — 926 citations
• View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017) — 619 citations
• View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018) — 490 citations
• Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (2019) — 543 citations
• Style Normalization and Restitution for Generalizable Person Re-Identification (2020) — 401 citations
• Inpaint Anything: Segment Anything Meets Image Inpainting (2023) — 320 citations
• PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network (2019) — 402 citations
• Relation-Aware Global Attention for Person Re-Identification (2019) — 590 citations
• Deeply-Fused Nets (2016) — 103 citations
• Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks (2016) — 225 citations