Xiaohan Ding

Computer Vision and Pattern Recognition · with Ding · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 824 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 197 citations into it; #5,736 of 21,575 by trust here, #6,031 by citations
• Artificial Intelligence — 144 citations into it; #8,187 of 35,345 by trust here, #9,170 by citations
• Model Architectures — 92 citations into it; #1,731 of 2,278 by trust here, #1,693 by citations
• Efficiency & Compression — 54 citations into it
• Optimization & Training — 34 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Media Technology — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Xiaohan Ding trusts overall

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

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

Who trusts Xiaohan Ding overall

1. Pavan Kumar Anasosalu Vasu
2. Chien-Yao Wang
3. Ao Wang
4. J. Gabriel
5. Chang-Bin Zhang
6. Alexey Bochkovskiy
7. X. Zhang
8. Jianqiang Xiao
9. Daiqi Shi
10. Weihao Yu

Papers from Xiaohan Ding's perspective

Ordered by how strongly the researchers Xiaohan Ding 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. 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

• ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks (2019) — 870 citations
• RepVGG: Making VGG-style ConvNets Great Again (2021) — 2,415 citations
• Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks (2018) — 127 citations
• Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (2022) — 781 citations
• Diverse Branch Block: Building a Convolution as an Inception-like Unit (2021) — 436 citations