Javen Qinfeng Shi

Computer Vision and Pattern Recognition · 2009–2023

A researcher in the Science corpus: 18 papers; trusts 1,147 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 440 citations into it; #3,593 of 21,575 by trust here, #2,340 by citations
• Artificial Intelligence — 272 citations into it; #22,814 of 35,345 by trust here, #8,525 by citations
• Model Architectures — 73 citations into it
• Efficiency & Compression — 67 citations into it
• Evaluation & Benchmarks — 60 citations into it
• Media Technology — 54 citations into it
• Generative Models — 42 citations into it
• Representation & Self-Supervised Learning — 40 citations into it; #3,050 of 3,145 by trust here, #2,757 by citations

Whom Javen Qinfeng Shi trusts overall

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

1. David A. McAllester
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. David G. Lowe
6. Diederik P. Kingma
7. Yoshua Bengio
8. Andrew Zisserman
9. Yann LeCun
10. Ross B. Girshick

Who trusts Javen Qinfeng Shi overall

1. Bernd Bohnet
2. Xu Chen
3. Hema Swetha Koppula
4. Aliaksei Severyn
5. James T. Wilson
6. Yu Kong
7. Kilian Q. Weinberger
8. Ruining He
9. Yongfeng Zhang
10. Bingbing Ni

Papers from Javen Qinfeng Shi's perspective

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

• Image-Based Recommendations on Styles and Substitutes (2015) — 2,890 citations
• Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models (2011) — 148 citations
• Hash Kernels for Structured Data (2009) — 240 citations
• Fast Supervised Hashing with Decision Trees for High-Dimensional Data (2014) — 428 citations
• Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks (2018) — 26 citations
• MOT20: A benchmark for multi object tracking in crowded scenes (2020) — 875 citations
• From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur (2016) — 436 citations
• Learn to Predict Sets Using Feed-Forward Neural Networks (2020) — 21 citations
• Joint Learning of Set Cardinality and State Distribution (2017) — 16 citations
• Inductive Hashing on Manifolds (2013) — 36 citations
• Real-time visual tracking using compressive sensing (2011) — 314 citations
• A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations (2023) — 518 citations
• Attention-Guided Network for Ghost-Free High Dynamic Range Imaging (2019) — 301 citations
• Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis (2019) — 69 citations
• The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs (2016) — 13 citations
• Joint Probabilistic Data Association Revisited (2015) — 365 citations
• A Hybrid Loss for Multiclass and Structured Prediction (2014) — 7 citations
• Hash Kernels (2009) — 89 citations