Jianyuan Guo
Computer Vision and Pattern Recognition · 2018–2023
A researcher in the Science corpus: 10 papers; trusts 1,543 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 587 citations into it; #6,861 of 21,575 by trust here, #2,754 by citations
- Artificial Intelligence — 398 citations into it; #20,055 of 35,345 by trust here, #8,706 by citations
- Model Architectures — 182 citations into it; #1,919 of 2,278 by trust here, #749 by citations
- Efficiency & Compression — 114 citations into it
- Optimization & Training — 44 citations into it
- Natural Language Processing — 34 citations into it
- Evaluation & Benchmarks — 32 citations into it
- Representation & Self-Supervised Learning — 32 citations into it
Whom Jianyuan Guo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Diederik P. Kingma
- Karen Simonyan
- Shaoqing Ren
Who trusts Jianyuan Guo overall
- Kai Han
- Yunhe Wang
- Yanyu Li
- Jianwei Yang
- Mang Wang
- Namuk Park
- Pavan Kumar Anasosalu Vasu
- Zhuofan Xia
- Xuran Pan
- Ning Zhang
Papers from Jianyuan Guo's perspective
Ordered by how strongly the researchers Jianyuan Guo 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)
- ImageNet: A large-scale hierarchical image database
- 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
- Transformer in Transformer (2021) — 2,223 citations
- GhostNet: More Features From Cheap Operations (2019) — 4,183 citations
- CMT: Convolutional Neural Networks Meet Vision Transformers (2021) — 951 citations
- OCNet: Object Context for Semantic Segmentation (2021) — 255 citations
- A Survey on Vision Transformer (2020) — 3,751 citations
- Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism (2023) — 614 citations
- Vision GNN: An Image is Worth Graph of Nodes (2022) — 633 citations
- GhostNetV2: Enhance Cheap Operation with Long-Range Attention (2022) — 579 citations
- Distilling Object Detectors via Decoupled Features (2021) — 258 citations
- Attribute-Aware Attention Model for Fine-grained Representation Learning (2018) — 132 citations