Jieneng Chen

Computer Vision and Pattern Recognition · 2019–2024

A researcher in the Science corpus: 9 papers; trusts 313 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 203 citations into it; #10,525 of 21,575 by trust here, #4,030 by citations
• Artificial Intelligence — 148 citations into it
• Model Architectures — 42 citations into it
• Biomedical Engineering — 23 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Reinforcement Learning — 18 citations into it
• Biophysics — 14 citations into it
• Optimization & Training — 9 citations into it

Whom Jieneng Chen trusts overall

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

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

Who trusts Jieneng Chen overall

1. Jeya Maria Jose Valanarasu
2. Junde Wu
3. Md Mostafijur Rahman
4. Hong-Yu Zhou
5. Jiacheng Ruan
6. Kaiwen Zhang
7. Bingzhi Chen
8. Xiaohong Huang
9. Haonan Wang
10. Xiangde Luo

Papers from Jieneng Chen's perspective

Ordered by how strongly the researchers Jieneng Chen 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (2021) — 5,990 citations
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation (2021) — 5,366 citations
• Semi-supervised Medical Image Segmentation through Dual-task Consistency (2020) — 796 citations
• TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers (2024) — 1,173 citations
• CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023) — 356 citations
• Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency (2022) — 383 citations
• TransFG: A Transformer Architecture for Fine-grained Recognition (2021) — 559 citations
• Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency (2020) — 325 citations
• Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent (2019) — 7 citations