Sheng Jin

Computer Vision and Pattern Recognition · with Xu · 2017–2023

A researcher in the Science corpus: 3 papers; trusts 934 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 232 citations into it; #13,916 of 21,575 by trust here, #8,952 by citations
• Artificial Intelligence — 136 citations into it; #22,504 of 35,345 by trust here, #14,181 by citations
• Multimodal Learning — 44 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Model Architectures — 19 citations into it
• Efficiency & Compression — 14 citations into it
• Large Language Models — 13 citations into it

Whom Sheng Jin trusts overall

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

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

Who trusts Sheng Jin overall

1. Zhendong Yang
2. Yifan Liu
3. Georgios Pavlakos
4. Minsoo Kang
5. Haoshu Fang
6. Xinlong Wang
7. Xizhou Zhu
8. Ce Zheng
9. Yi Wei
10. Zhengxia Zou

Papers from Sheng Jin's perspective

Ordered by how strongly the researchers Sheng Jin 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. 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

• Mimicking Very Efficient Network for Object Detection (2017) — 374 citations
• Whole-Body Human Pose Estimation in the Wild (2020) — 351 citations
• Vision-Language Models for Vision Tasks: A Survey (2023) — 1,489 citations