Xianzhi Du
Computer Vision and Pattern Recognition · with Li · 2019–2022
A researcher in the Science corpus: 7 papers; trusts 848 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 304 citations into it
- Artificial Intelligence — 226 citations into it
- Model Architectures — 118 citations into it
- Optimization & Training — 49 citations into it
- Efficiency & Compression — 25 citations into it
- Evaluation & Benchmarks — 24 citations into it
- Representation & Self-Supervised Learning — 21 citations into it
- Media Technology — 7 citations into it
Whom Xianzhi Du trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Ross B. Girshick
- Geoffrey E. Hinton
- Tsung-Yi Lin
- Quoc V. Le
- Karen Simonyan
- Yoshua Bengio
- Christian Szegedy
- Yann LeCun
Who trusts Xianzhi Du overall
- Chien-Yao Wang
- Bowen Cheng
- Golnaz Ghiasi
- Alexey Bochkovskiy
- Shen Yan
- Kihyuk Sohn
- Xiyang Dai
- A. Srinivas
- Chengqi Lyu
- Tsung-Yi Lin
Papers from Xianzhi Du's perspective
Ordered by how strongly the researchers Xianzhi Du 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Revisiting ResNets: Improved Training and Scaling Strategies (2021) — 365 citations
- SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization (2019) — 184 citations
- A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation (2021) — 44 citations
- Simple Training Strategies and Model Scaling for Object Detection (2021) — 52 citations
- Revisiting 3D ResNets for Video Recognition (2021) — 22 citations
- Auto-scaling Vision Transformers without Training (2022) — 28 citations
- Dilated SpineNet for Semantic Segmentation (2021) — 8 citations