Jingkuan Song

Computer Vision and Pattern Recognition · 2011–2022

A researcher in the Science corpus: 18 papers; trusts 1,689 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 486 citations into it; #3,979 of 21,575 by trust here, #3,598 by citations
• Artificial Intelligence — 331 citations into it; #21,390 of 35,345 by trust here, #7,010 by citations
• Representation & Self-Supervised Learning — 85 citations into it
• Information Retrieval & Search — 70 citations into it; #605 of 1,022 by trust here, #512 by citations
• Efficiency & Compression — 57 citations into it
• Information Systems — 47 citations into it; #6,279 of 8,771 by trust here, #2,260 by citations
• Natural Language Processing — 41 citations into it; #4,430 of 5,688 by trust here, #3,638 by citations
• Signal Processing — 41 citations into it

Whom Jingkuan Song trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. David G. Lowe

Who trusts Jingkuan Song overall

1. Shiv Ram Dubey
2. Erkun Yang
3. Cheng Deng
4. Lianli Gao
5. Nantheera Anantrasirichai
6. Dongqing Zhang
7. Yuqing Ma
8. Jingdong Wang
9. Jie Gui
10. Mingsheng Long

Papers from Jingkuan Song's perspective

Ordered by how strongly the researchers Jingkuan Song 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. 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

• Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering (2019) — 299 citations
• A Survey on Learning to Hash (2016) — 1,084 citations
• Multiple feature hashing for real-time large scale near-duplicate video retrieval (2011) — 327 citations
• Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015) — 541 citations
• Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013) — 616 citations
• Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis (2019) — 46 citations
• Binary Neural Networks: A Survey (2020) — 577 citations
• MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning (2018) — 122 citations
• NAIS: Neural Attentive Item Similarity Model for Recommendation (2018) — 531 citations
• Class Gradient Projection For Continual Learning (2022) — 26 citations
• Progressive Meta-Learning With Curriculum (2022) — 71 citations
• Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression (2022) — 86 citations
• Curriculum-Based Meta-learning (2021) — 28 citations
• Forward and Backward Information Retention for Accurate Binary Neural Networks (2019) — 399 citations
• Binary Generative Adversarial Networks for Image Retrieval (2017) — 205 citations
• Learning in high-dimensional multimedia data: the state of the art (2015) — 129 citations
• Hashing for Similarity Search: A Survey (2014) — 577 citations
• Optimized Cartesian K-Means (2014) — 121 citations