Bowen Cheng

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 9 papers; trusts 1,611 researchers and is trusted by 478 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 452 citations into it; #1,910 of 21,575 by trust here, #2,191 by citations
• Artificial Intelligence — 212 citations into it; #2,784 of 35,345 by trust here, #3,289 by citations
• Model Architectures — 72 citations into it; #1,805 of 2,278 by trust here, #906 by citations
• Evaluation & Benchmarks — 56 citations into it; #2,315 of 3,147 by trust here, #2,393 by citations
• Optimization & Training — 26 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Aerospace Engineering — 17 citations into it
• 3D Vision & Pose — 11 citations into it

Whom Bowen Cheng trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Liang-Chieh Chen
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Shaoqing Ren
7. Tsung-Yi Lin
8. Diederik P. Kingma
9. Yann LeCun
10. Piotr Doll'ar

Who trusts Bowen Cheng overall

1. Huiyu Wang
2. Xueyan Zou
3. Qihang Yu
4. Liang-Chieh Chen
5. Ting-Li Chen
6. Beomyoung Kim
7. Jianwei Yang
8. Yongming Rao
9. Zekang Zhang
10. Meng-Hao Guo

Papers from Bowen Cheng's perspective

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

Their papers

• Masked-attention Mask Transformer for Universal Image Segmentation (2021) — 4,195 citations
• Per-Pixel Classification is Not All You Need for Semantic Segmentation (2021) — 2,101 citations
• Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation (2019) — 690 citations
• Revisiting RCNN: On Awakening the Classification Power of Faster RCNN (2018) — 241 citations
• SPGNet: Semantic Prediction Guidance for Scene Parsing (2019) — 117 citations
• HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (2019) — 832 citations
• Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation (2020) — 121 citations
• Boundary IoU: Improving Object-Centric Image Segmentation Evaluation (2021) — 491 citations
• Pointly-Supervised Instance Segmentation (2021) — 149 citations