Bing Li

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2010–2021

A researcher in the Science corpus: 11 papers; trusts 728 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 365 citations into it; #10,940 of 21,575 by trust here, #4,502 by citations
• Artificial Intelligence — 116 citations into it; #27,117 of 35,345 by trust here, #14,718 by citations
• Evaluation & Benchmarks — 46 citations into it
• Model Architectures — 29 citations into it
• Natural Language Processing — 29 citations into it
• Human-Computer Interaction — 27 citations into it
• Cognitive Neuroscience — 25 citations into it; #6,446 of 10,852 by trust here, #4,925 by citations
• Sensory Systems — 19 citations into it; #456 of 760 by trust here, #320 by citations

Whom Bing Li trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Bing Li overall

1. Deng-Ping Fan
2. Hao Chen
3. Runmin Cong
4. Jun Liu
5. Jason Kuen
6. H. Wang
7. Keren Fu
8. Ming-Ming Cheng
9. Yongri Piao
10. Nian Liu

Papers from Bing Li's perspective

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

Their papers

• Object Relational Graph With Teacher-Recommended Learning for Video Captioning (2020) — 316 citations
• Knowledge Distillation via Instance Relationship Graph (2019) — 329 citations
• The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
• Open-book Video Captioning with Retrieve-Copy-Generate Network (2021) — 118 citations
• Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition (2021) — 930 citations
• RGBD Salient Object Detection: A Benchmark and Algorithms (2014) — 648 citations
• The Seventh Visual Object Tracking VOT2019 Challenge Results (2019) — 486 citations
• Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition (2016) — 101 citations
• Rethinking the Competition Between Detection and ReID in Multiobject Tracking (2020) — 396 citations
• Salient Object Detection via Structured Matrix Decomposition (2017) — 349 citations
• Identifying Multi-instance Outliers (2010) — 17 citations