Haotian Liu

Computer Vision and Pattern Recognition · 2022–2023

A researcher in the Science corpus: 9 papers; trusts 1,196 researchers and is trusted by 161 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 386 citations into it; #2,819 of 21,575 by trust here, #1,468 by citations
• Artificial Intelligence — 326 citations into it; #4,075 of 35,345 by trust here, #2,001 by citations
• Multimodal Learning — 165 citations into it; #142 of 1,211 by trust here, #126 by citations
• Large Language Models — 101 citations into it; #372 of 799 by trust here, #427 by citations
• Evaluation & Benchmarks — 75 citations into it; #2,753 of 3,147 by trust here, #2,590 by citations
• Representation & Self-Supervised Learning — 58 citations into it
• Generative Models — 54 citations into it
• Natural Language Processing — 36 citations into it

Whom Haotian Liu trusts overall

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

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

Who trusts Haotian Liu overall

1. Bo Li
2. Lin Chen
3. Renrui Zhang
4. Penghao Wu
5. Chunyuan Li
6. Yanwei Li
7. Yuanhan Zhang
8. Peng Gao
9. Hao Shao
10. Bin Lin

Papers from Haotian Liu's perspective

Ordered by how strongly the researchers Haotian Liu 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Visual Instruction Tuning (2023) — 10,396 citations
• Improved Baselines with Visual Instruction Tuning (2023) — 5,499 citations
• An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models (2023) — 44 citations
• GLIGEN: Open-Set Grounded Text-to-Image Generation (2023) — 965 citations
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day (2023) — 1,910 citations
• Aligning Large Multimodal Models with Factually Augmented RLHF (2023) — 811 citations
• End-to-End Instance Edge Detection (2022)
• Learning Customized Visual Models with Retrieval-Augmented Knowledge (2023) — 88 citations
• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022) — 195 citations