Yehao Li
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 8 papers; trusts 1,304 researchers and is trusted by 84 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 231 citations into it; #3,244 of 21,575 by trust here, #2,888 by citations
- Artificial Intelligence — 154 citations into it; #6,747 of 35,345 by trust here, #5,834 by citations
- Natural Language Processing — 75 citations into it
- Multimodal Learning — 71 citations into it; #134 of 1,211 by trust here, #284 by citations
- Model Architectures — 64 citations into it
- Evaluation & Benchmarks — 49 citations into it
- Representation & Self-Supervised Learning — 23 citations into it
- Optimization & Training — 17 citations into it
Whom Yehao Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Diederik P. Kingma
- Ilya Sutskever
- Yoshua Bengio
- Karen Simonyan
- Yann LeCun
- Oriol Vinyals
Who trusts Yehao Li overall
- Ting Yao
- Marcella Cornia
- Matteo Stefanini
- Yingwei Pan
- Xu Yang
- Lorenzo Baraldi
- Tao Mei
- Ruotian Luo
- Hanwang Zhang
- Xiaowei Hu
Papers from Yehao Li's perspective
Ordered by how strongly the researchers Yehao Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Boosting Image Captioning with Attributes (2016) — 656 citations
- Exploring Visual Relationship for Image Captioning (2018) — 923 citations
- Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects (2017) — 154 citations
- Transferrable Prototypical Networks for Unsupervised Domain Adaptation (2019) — 395 citations
- X-Linear Attention Networks for Image Captioning (2020) — 635 citations
- Pointing Novel Objects in Image Captioning (2019) — 74 citations
- Hierarchy Parsing for Image Captioning (2019) — 183 citations
- Contextual Transformer Networks for Visual Recognition (2021) — 671 citations