Jingbo Wang

Computer Vision and Pattern Recognition · with Yu · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 929 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 213 citations into it; #6,714 of 21,575 by trust here, #3,191 by citations
• Artificial Intelligence — 95 citations into it; #34,926 of 35,345 by trust here, #9,170 by citations
• Model Architectures — 40 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Media Technology — 16 citations into it
• Efficiency & Compression — 9 citations into it
• Biophysics — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Jingbo Wang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Ilya Sutskever
6. Yoshua Bengio
7. Karen Simonyan
8. Christian Szegedy
9. Liang-Chieh Chen
10. Yann LeCun

Who trusts Jingbo Wang overall

1. Changqian Yu
2. Huihui Pan
3. Xiangtai Li
4. Hanchao Li
5. Zilong Huang
6. Junjun He
7. Yuval Nirkin
8. Yanwei Li
9. Tianyi Wu
10. Jiacong Xu

Papers from Jingbo Wang's perspective

Ordered by how strongly the researchers Jingbo Wang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation (2018) — 2,729 citations
• Learning a Discriminative Feature Network for Semantic Segmentation (2018) — 818 citations
• An End-To-End Network for Panoptic Segmentation (2019) — 150 citations
• BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation (2020) — 1,654 citations
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation (2020) — 449 citations