Yuanhan Zhang
Computer Vision and Pattern Recognition · 2023–2024
A researcher in the Science corpus: 11 papers; trusts 682 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 472 citations into it; #18,580 of 21,575 by trust here, #4,105 by citations
- Artificial Intelligence — 364 citations into it; #30,251 of 35,345 by trust here, #4,370 by citations
- Multimodal Learning — 182 citations into it; #980 of 1,211 by trust here, #295 by citations
- Large Language Models — 113 citations into it
- Evaluation & Benchmarks — 105 citations into it
- Natural Language Processing — 50 citations into it
- Question Answering & Reading Comprehension — 42 citations into it
- Representation & Self-Supervised Learning — 17 citations into it
Whom Yuanhan Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Yann LeCun
- Karen Simonyan
- Alec Radford
Who trusts Yuanhan Zhang overall
- Bo Li
- Renrui Zhang
- Penghao Wu
- Deyao Zhu
- Bo Zhao
- Anas Awadalla
- Peng Xu
- Haotian Liu
- Zhiqing Sun
- Saining Xie
Papers from Yuanhan Zhang's perspective
Ordered by how strongly the researchers Yuanhan Zhang 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
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- MMBench: Is Your Multi-modal Model an All-around Player? (2023) — 2,293 citations
- MIMIC-IT: Multi-Modal In-Context Instruction Tuning (2023) — 310 citations
- VBench: Comprehensive Benchmark Suite for Video Generative Models (2023) — 1,673 citations
- Otter: A Multi-Modal Model With In-Context Instruction Tuning (2023) — 692 citations
- LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2024) — 331 citations
- LLaVA-OneVision: Easy Visual Task Transfer (2024) — 2,742 citations
- LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models (2024) — 619 citations
- Long Context Transfer from Language to Vision (2024) — 531 citations
- Video Instruction Tuning With Synthetic Data (2024) — 420 citations
- LLaVA-Video: Video Instruction Tuning With Synthetic Data (2024) — 439 citations
- OtterHD: A High-Resolution Multi-modality Model (2023) — 87 citations