Zhengyuan Yang
Computer Vision and Pattern Recognition · 2019–2023
A researcher in the Science corpus: 17 papers; trusts 1,536 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 707 citations into it; #4,710 of 21,575 by trust here, #1,654 by citations
- Artificial Intelligence — 663 citations into it; #7,160 of 35,345 by trust here, #2,243 by citations
- Multimodal Learning — 430 citations into it; #435 of 1,211 by trust here, #165 by citations
- Natural Language Processing — 178 citations into it
- Large Language Models — 148 citations into it
- Evaluation & Benchmarks — 123 citations into it
- Representation & Self-Supervised Learning — 67 citations into it
- Model Architectures — 59 citations into it
Whom Zhengyuan Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Karen Simonyan
- Shaoqing Ren
- Yann LeCun
Who trusts Zhengyuan Yang overall
- Jianfeng Wang
- Muchen Li
- Zhe Gan
- Yushi Hu
- Haotian Liu
- Jiajun Deng
- Weicheng Kuo
- Chang Liu
- Xiaowei Hu
- Ye Du
Papers from Zhengyuan Yang's perspective
Ordered by how strongly the researchers Zhengyuan Yang 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- 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
- An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA (2021) — 537 citations
- Scaling Up Vision-Language Pretraining for Image Captioning (2021) — 335 citations
- GIT: A Generative Image-to-text Transformer for Vision and Language (2022) — 787 citations
- UFO: A UniFied TransfOrmer for Vision-Language Representation Learning (2021) — 65 citations
- MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action (2023) — 603 citations
- TAP: Text-Aware Pre-training for Text-VQA and Text-Caption (2020) — 164 citations
- A Fast and Accurate One-Stage Approach to Visual Grounding (2019) — 449 citations
- UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling (2021) — 147 citations
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) (2023) — 973 citations
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities (2023) — 1,322 citations
- TransVG: End-to-End Visual Grounding with Transformers (2021) — 494 citations
- Prompting GPT-3 To Be Reliable (2022) — 378 citations
- Multimodal Foundation Models: From Specialists to General-Purpose Assistants (2023) — 394 citations
- Improving One-stage Visual Grounding by Recursive Sub-query Construction (2020) — 310 citations
- PromptCap: Prompt-Guided Task-Aware Image Captioning (2022) — 142 citations
- ReCo: Region-Controlled Text-to-Image Generation (2022) — 214 citations
- Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling (2021) — 39 citations