Liunian Harold Li
Computer Vision and Pattern Recognition · 2019–2022
A researcher in the Science corpus: 9 papers; trusts 1,010 researchers and is trusted by 441 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 438 citations into it; #2,001 of 21,575 by trust here, #1,452 by citations
- Artificial Intelligence — 371 citations into it; #3,210 of 35,345 by trust here, #1,937 by citations
- Multimodal Learning — 190 citations into it; #126 of 1,211 by trust here, #117 by citations
- Natural Language Processing — 104 citations into it
- Evaluation & Benchmarks — 85 citations into it; #2,752 of 3,147 by trust here, #2,590 by citations
- Representation & Self-Supervised Learning — 82 citations into it
- Model Architectures — 56 citations into it
- Large Language Models — 33 citations into it
Whom Liunian Harold Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Shaoqing Ren
- Ilya Sutskever
- Yann LeCun
- Jimmy Ba
Who trusts Liunian Harold Li overall
- Zhe Gan
- Ronghang Hu
- Linjie Li
- Yen-Chun Chen
- Pengchuan Zhang
- Xiaowei Hu
- Jianfeng Wang
- Jiasen Lu
- Jianwei Yang
- Wenliang Dai
Papers from Liunian Harold Li's perspective
Ordered by how strongly the researchers Liunian Harold Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- VisualBERT: A Simple and Performant Baseline for Vision and Language (2019) — 2,355 citations
- Grounded Language-Image Pre-training (2021) — 1,697 citations
- How Much Can CLIP Benefit Vision-and-Language Tasks? (2021) — 516 citations
- RegionCLIP: Region-based Language-Image Pretraining (2021) — 921 citations
- GLIPv2: Unifying Localization and Vision-Language Understanding (2022) — 392 citations
- What Does BERT with Vision Look At? (2020) — 167 citations
- Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions (2021) — 54 citations
- On the Paradox of Learning to Reason from Data (2022) — 145 citations
- ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022) — 195 citations