Jianfeng Wang
Computer Vision and Pattern Recognition · with Wang · 2014–2023
A researcher in the Science corpus: 24 papers; trusts 1,962 researchers and is trusted by 79 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 1,143 citations into it; #3,457 of 21,575 by trust here, #1,132 by citations
- Artificial Intelligence — 980 citations into it; #7,652 of 35,345 by trust here, #1,547 by citations
- Multimodal Learning — 635 citations into it; #316 of 1,211 by trust here, #135 by citations
- Natural Language Processing — 228 citations into it
- Evaluation & Benchmarks — 209 citations into it
- Large Language Models — 199 citations into it
- Representation & Self-Supervised Learning — 178 citations into it; #3,101 of 3,145 by trust here, #2,558 by citations
- Model Architectures — 112 citations into it
Whom Jianfeng Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Yann LeCun
- Karen Simonyan
- Shaoqing Ren
Who trusts Jianfeng Wang overall
- Zhengyuan Yang
- Xiaowei Hu
- Zhe Gan
- Antoine Yang
- Zhiyuan Fang
- Yushi Hu
- Linjie Li
- Ting Zhang
- Jingdong Wang
- Haotian Liu
Papers from Jianfeng Wang's perspective
Ordered by how strongly the researchers Jianfeng Wang 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
- Florence: A New Foundation Model for Computer Vision (2021) — 1,142 citations
- 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
- End-to-End Semi-Supervised Object Detection with Soft Teacher (2021) — 662 citations
- MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action (2023) — 603 citations
- An Empirical Study of Training End-to-End Vision-and-Language Transformers (2021) — 462 citations
- TAP: Text-Aware Pre-training for Text-VQA and Text-Caption (2020) — 164 citations
- SEED: Self-supervised Distillation For Visual Representation (2021) — 217 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
- Generalized Decoding for Pixel, Image, and Language (2022) — 380 citations
- Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone (2022) — 167 citations
- MiniVLM: A Smaller and Faster Vision-Language Model (2020) — 76 citations
- Compressing Visual-linguistic Model via Knowledge Distillation (2021) — 124 citations
- Injecting Semantic Concepts into End-to-End Image Captioning (2021) — 127 citations
- Prompting GPT-3 To Be Reliable (2022) — 378 citations
- Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (2023) — 519 citations