Yunhe Wang

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 29 papers; trusts 2,038 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 1,064 citations into it; #4,688 of 21,575 by trust here, #1,562 by citations
• Artificial Intelligence — 743 citations into it; #18,041 of 35,345 by trust here, #7,858 by citations
• Efficiency & Compression — 328 citations into it
• Model Architectures — 282 citations into it; #1,650 of 2,278 by trust here, #686 by citations
• Optimization & Training — 111 citations into it
• Media Technology — 91 citations into it
• Generative Models — 78 citations into it
• Evaluation & Benchmarks — 53 citations into it

Whom Yunhe Wang 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. Karen Simonyan
6. Ilya Sutskever
7. Yoshua Bengio
8. Yann LeCun
9. Diederik P. Kingma
10. Shaoqing Ren

Who trusts Yunhe Wang overall

1. Kai Han
2. Hanting Chen
3. Jianping Gou
4. Dacheng Tao
5. Chang Xu
6. Jianyuan Guo
7. Chao Xu
8. Yehui Tang
9. Zhaohui Yang
10. Shan You

Papers from Yunhe Wang's perspective

Ordered by how strongly the researchers Yunhe Wang 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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

• Transformer in Transformer (2021) — 2,223 citations
• Pre-Trained Image Processing Transformer (2020) — 2,255 citations
• GhostNet: More Features From Cheap Operations (2019) — 4,183 citations
• CMT: Convolutional Neural Networks Meet Vision Transformers (2021) — 951 citations
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020) — 111 citations
• Co-Evolutionary Compression for Unpaired Image Translation (2019) — 93 citations
• CARS: Continuous Evolution for Efficient Neural Architecture Search (2019) — 269 citations
• A Survey on Vision Transformer (2020) — 3,751 citations
• AdderNet: Do We Really Need Multiplications in Deep Learning? (2019) — 236 citations
• Post-Training Quantization for Vision Transformer (2021) — 505 citations
• Learning Instance-wise Sparsity for Accelerating Deep Models (2019) — 30 citations
• GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image (2023) — 419 citations
• Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism (2023) — 614 citations
• Vision GNN: An Image is Worth Graph of Nodes (2022) — 633 citations
• GhostNetV2: Enhance Cheap Operation with Long-Range Attention (2022) — 579 citations
• Distilling Object Detectors via Decoupled Features (2021) — 258 citations
• Adversarially Robust Neural Architectures (2020) — 55 citations
• Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts (2020) — 36 citations
• Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks (2020) — 41 citations
• On Positive-Unlabeled Classification in GAN (2020) — 39 citations