Zhiyuan Fang
Computer Vision and Pattern Recognition · 2016–2022
A researcher in the Science corpus: 6 papers; trusts 955 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 271 citations into it; #9,085 of 21,575 by trust here, #5,843 by citations
- Artificial Intelligence — 204 citations into it; #19,460 of 35,345 by trust here, #14,181 by citations
- Multimodal Learning — 85 citations into it; #720 of 1,211 by trust here, #962 by citations
- Representation & Self-Supervised Learning — 54 citations into it; #3,100 of 3,145 by trust here, #2,558 by citations
- Natural Language Processing — 50 citations into it
- Evaluation & Benchmarks — 49 citations into it
- Efficiency & Compression — 38 citations into it
- Model Architectures — 28 citations into it
Whom Zhiyuan Fang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Shaoqing Ren
- Yann LeCun
- Karen Simonyan
Who trusts Zhiyuan Fang overall
- Jianfeng Wang
- Zhiqiang Shen
- Xiaowei Hu
- Zhe Gan
- Bingchen Zhao
- Zhengyuan Yang
- Lijuan Wang
- Jabeen Summaira
- Jiankang Deng
- Zicheng Liu
Papers from Zhiyuan Fang's perspective
Ordered by how strongly the researchers Zhiyuan Fang 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
- Going deeper with convolutions
Their papers
- Range Loss for Deep Face Recognition with Long-Tailed Training Data (2016) — 472 citations
- SEED: Self-supervised Distillation For Visual Representation (2021) — 217 citations
- Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning (2020) — 68 citations
- Compressing Visual-linguistic Model via Knowledge Distillation (2021) — 124 citations
- Injecting Semantic Concepts into End-to-End Image Captioning (2021) — 127 citations
- Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation (2022) — 51 citations