Dengke Dong

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 309 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #11,169 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 45 citations into it; #20,061 of 35,345 by trust here, #10,065 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Model Architectures — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Optimization & Training — 11 citations into it

Whom Dengke Dong trusts overall

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

1. Geoffrey E. Hinton
2. Christian Szegedy
3. Sergey Ioffe
4. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Yoshua Bengio
8. Ross B. Girshick
9. Jia Deng
10. Andrew Zisserman

Who trusts Dengke Dong overall

1. Timo Milbich
2. Karsten Roth
3. Mahmut Kaya
4. Kevin Musgrave

Papers from Dengke Dong's perspective

Ordered by how strongly the researchers Dengke Dong 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Going deeper with convolutions
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (2018) — 383 citations
• Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning (2019) — 909 citations
• Deep Metric Learning with Hierarchical Triplet Loss (2018) — 465 citations