Jin-shan Pan

Computer Vision and Pattern Recognition · 2014–2023

A researcher in the Science corpus: 16 papers; trusts 1,267 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 432 citations into it; #3,778 of 21,575 by trust here, #2,001 by citations
• Media Technology — 197 citations into it; #566 of 3,621 by trust here, #116 by citations
• Artificial Intelligence — 62 citations into it
• Model Architectures — 42 citations into it
• Generative Models — 26 citations into it
• Optimization & Training — 21 citations into it
• Computational Mechanics — 20 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Jin-shan Pan trusts overall

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

1. Li Xu
2. Kaiming He
3. Diederik P. Kingma
4. Rob Fergus
5. Geoffrey E. Hinton
6. Anat Levin
7. Alex Krizhevsky
8. Ross B. Girshick
9. Karen Simonyan
10. Yair Weiss

Who trusts Jin-shan Pan overall

1. Wenqi Ren
2. Boyu Lu
3. Jiawei Zhang
4. Xiangyu Xu
5. Yanyang Yan
6. Zewei He
7. Ming-Hsuan Yang
8. Kai Zhang
9. Jaesung Rim
10. Kaihao Zhang

Papers from Jin-shan Pan's perspective

Ordered by how strongly the researchers Jin-shan Pan 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Single Image Dehazing via Multi-scale Convolutional Neural Networks (2016) — 1,713 citations
• NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (2017) — 1,901 citations
• Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network (2016) — 168 citations
• Blind Image Deblurring Using Dark Channel Prior (2016) — 785 citations
• Deblurring Text Images via L0-Regularized Intensity and Gradient Prior (2014) — 483 citations
• Single Image Dehazing via Conditional Generative Adversarial Network (2018) — 405 citations
• Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks (2018) — 381 citations
• Gated Fusion Network for Single Image Dehazing (2018) — 881 citations
• Learning to Super-Resolve Blurry Face and Text Images (2017) — 223 citations
• Spatio-Temporal Filter Adaptive Network for Video Deblurring (2019) — 232 citations
• Learning A Sparse Transformer Network for Effective Image Deraining (2023) — 545 citations
• Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring (2022) — 374 citations
• Image De-raining via Continual Learning (2021) — 66 citations
• Multi-Scale Boosted Dehazing Network With Dense Feature Fusion (2020) — 1,022 citations
• Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019) — 361 citations
• $L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond (2017) — 304 citations