Weiyao Wang

Computer Vision and Pattern Recognition · with Feiszli · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 782 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 140 citations into it; #17,567 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 64 citations into it; #28,715 of 35,345 by trust here, #19,018 by citations
• Evaluation & Benchmarks — 28 citations into it
• Multimodal Learning — 15 citations into it; #942 of 1,211 by trust here, #920 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 12 citations into it
• Signal Processing — 9 citations into it
• Optimization & Training — 7 citations into it

Whom Weiyao Wang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Andrew Zisserman
6. Karen Simonyan
7. Piotr Doll'ar
8. Yann LeCun
9. Shaoqing Ren
10. Diederik P. Kingma

Who trusts Weiyao Wang overall

1. Haytham M. Fayek
2. Douwe Kiela
3. Zongbo Han
4. Xiaokang Peng
5. Yu Huang
6. Andrew Jaegle
7. Lei Ke
8. Anurag Kumar
9. Ahmad Darkhalil
10. Changqing Zhang

Papers from Weiyao Wang's perspective

Ordered by how strongly the researchers Weiyao Wang 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. 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

• What Makes Training Multi-Modal Classification Networks Hard? (2019) — 665 citations
• Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation (2021) — 157 citations
• Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity (2022) — 56 citations