Kunpeng Li

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

A researcher in the Science corpus: 6 papers; trusts 929 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 194 citations into it; #4,602 of 21,575 by trust here, #2,926 by citations
• Artificial Intelligence — 99 citations into it; #33,334 of 35,345 by trust here, #9,760 by citations
• Media Technology — 52 citations into it; #512 of 3,621 by trust here, #259 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Multimodal Learning — 23 citations into it
• Model Architectures — 22 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Generative Models — 15 citations into it

Whom Kunpeng Li trusts overall

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

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

Who trusts Kunpeng Li overall

1. Kai Zhang
2. Yulun Zhang
3. Syed Waqas Zamir
4. Jingyun Liang
5. Saeed Anwar
6. Jinjin Gu
7. Radu Timofte
8. Zizhao Zhang
9. Junsuk Choe
10. Jie Liu

Papers from Kunpeng Li's perspective

Ordered by how strongly the researchers Kunpeng Li 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Image Super-Resolution Using Very Deep Residual Channel Attention Networks (2018) — 5,405 citations
• Tell Me Where to Look: Guided Attention Inference Network (2018) — 568 citations
• Residual Non-local Attention Networks for Image Restoration (2019) — 777 citations
• Visual Semantic Reasoning for Image-Text Matching (2019) — 614 citations
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP (2022) — 767 citations
• Adversarial Feature Hallucination Networks for Few-Shot Learning (2020) — 302 citations