Houdong Hu
Computer Vision and Pattern Recognition · 2018–2023
A researcher in the Science corpus: 7 papers; trusts 745 researchers and is trusted by 330 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 340 citations into it; #2,204 of 21,575 by trust here, #1,098 by citations
- Artificial Intelligence — 254 citations into it; #3,361 of 35,345 by trust here, #1,463 by citations
- Multimodal Learning — 155 citations into it; #130 of 1,211 by trust here, #107 by citations
- Representation & Self-Supervised Learning — 72 citations into it
- Natural Language Processing — 69 citations into it
- Evaluation & Benchmarks — 61 citations into it; #2,743 of 3,147 by trust here, #2,442 by citations
- Model Architectures — 39 citations into it
- Large Language Models — 36 citations into it
Whom Houdong Hu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Shaoqing Ren
- Karen Simonyan
Who trusts Houdong Hu overall
- Zhe Gan
- Jianwei Yang
- Jianfeng Wang
- Xiaowei Hu
- Linjie Li
- Yen-Chun Chen
- Pengchuan Zhang
- Zhengyuan Yang
- Matteo Stefanini
- Marcella Cornia
Papers from Houdong Hu's perspective
Ordered by how strongly the researchers Houdong Hu 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
- Going deeper with convolutions
Their papers
- Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks (2020) — 2,259 citations
- Unified Vision-Language Pre-Training for Image Captioning and VQA (2019) — 1,067 citations
- Florence: A New Foundation Model for Computer Vision (2021) — 1,142 citations
- Stacked Cross Attention for Image-Text Matching (2018) — 1,394 citations
- Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks (2023) — 565 citations
- ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022) — 195 citations
- Image Scene Graph Generation (SGG) Benchmark (2021) — 42 citations