Bin Li

Computer Vision and Pattern Recognition · 2009–2023

A researcher in the Science corpus: 8 papers; trusts 458 researchers and is trusted by 456 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 230 citations into it; #1,995 of 21,575 by trust here, #1,131 by citations
• Artificial Intelligence — 221 citations into it; #3,903 of 35,345 by trust here, #1,452 by citations
• Model Architectures — 39 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Optimization & Training — 12 citations into it
• Generative Models — 11 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Bin Li trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Paul Resnick
8. Arthur P. Dempster
9. Yann LeCun
10. Ilya Sutskever

Who trusts Bin Li overall

1. Weike Pan
2. Qiang Yang
3. Yue Shi
4. Sotirios P. Chatzis
5. Emilio Ferrara
6. Evan Wei Xiang
7. Sheng Gao
8. Qian Zhang
9. Iván Cantador
10. Qian Xu

Papers from Bin Li's perspective

Ordered by how strongly the researchers Bin Li 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• VL-BERT: Pre-training of Generic Visual-Linguistic Representations (2019) — 1,848 citations
• Deformable DETR: Deformable Transformers for End-to-End Object Detection (2020) — 7,924 citations
• Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks (2023) — 3,154 citations
• Transfer learning for collaborative filtering via a rating-matrix generative model (2009) — 385 citations
• Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory (2023) — 345 citations
• Self-adaptive learning based particle swarm optimization (2011) — 336 citations
• Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation (2019) — 25 citations
• Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction (2009) — 457 citations