Kaipeng Zhang

Computer Vision and Pattern Recognition · with Qiao · 2016–2023

A researcher in the Science corpus: 7 papers; trusts 696 researchers and is trusted by 270 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 256 citations into it; #2,395 of 21,575 by trust here, #2,001 by citations
• Artificial Intelligence — 145 citations into it
• Evaluation & Benchmarks — 51 citations into it
• Signal Processing — 45 citations into it; #387 of 7,572 by trust here, #242 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #828 of 3,145 by trust here, #693 by citations
• Efficiency & Compression — 16 citations into it
• Media Technology — 13 citations into it
• Optimization & Training — 11 citations into it

Whom Kaipeng Zhang trusts overall

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

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

Who trusts Kaipeng Zhang overall

1. Yandong Wen
2. Weiyang Liu
3. Eden Belouadah
4. Weihong Deng
5. Jiankang Deng
6. Kai-Hong Wang
7. Feng Wang
8. Yu Qiao
9. Anjith George
10. Amir Zadeh

Papers from Kaipeng Zhang's perspective

Ordered by how strongly the researchers Kaipeng Zhang 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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 Discriminative Feature Learning Approach for Deep Face Recognition (2016) — 4,029 citations
• Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks (2016) — 5,675 citations
• ImageBind-LLM: Multi-modality Instruction Tuning (2023) — 181 citations
• LVLM-EHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models (2023) — 280 citations
• Gender and Smile Classification Using Deep Convolutional Neural Networks (2016) — 69 citations
• OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023) — 460 citations
• A Comprehensive Study on Center Loss for Deep Face Recognition (2019) — 96 citations