Jun Chen

Computer Vision and Pattern Recognition · with Elhoseiny · 2021–2024

A researcher in the Science corpus: 4 papers; trusts 424 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 154 citations into it; #11,739 of 21,575 by trust here, #3,035 by citations
• Artificial Intelligence — 151 citations into it; #19,863 of 35,345 by trust here, #3,998 by citations
• Multimodal Learning — 98 citations into it; #838 of 1,211 by trust here, #302 by citations
• Large Language Models — 64 citations into it; #504 of 799 by trust here, #347 by citations
• Natural Language Processing — 45 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Medical Imaging — 5 citations into it

Whom Jun Chen trusts overall

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

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

Who trusts Jun Chen overall

1. Wei Zhang
2. Renrui Zhang
3. Roberto Gozalo-Brizuela
4. Lin Chen
5. Yixuan Su
6. Kartik Kuckreja
7. Yao Mu
8. Miaoxin Cai
9. Jiasen Lu
10. Chameleon Team

Papers from Jun Chen's perspective

Ordered by how strongly the researchers Jun Chen 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models (2023) — 3,250 citations
• VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning (2021) — 299 citations
• MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning (2023) — 726 citations
• Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents (2024)