Qiming Zhang
Computer Vision and Pattern Recognition · with Zhang · 2017–2022
A researcher in the Science corpus: 7 papers; trusts 1,244 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 331 citations into it; #7,122 of 21,575 by trust here, #6,671 by citations
- Artificial Intelligence — 214 citations into it; #11,084 of 35,345 by trust here, #11,072 by citations
- Model Architectures — 102 citations into it
- Efficiency & Compression — 60 citations into it
- Evaluation & Benchmarks — 25 citations into it
- Media Technology — 25 citations into it
- Electrical and Electronic Engineering — 23 citations into it; #10,406 of 16,539 by trust here, #10,998 by citations
- Representation & Self-Supervised Learning — 22 citations into it
Whom Qiming Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Ross B. Girshick
- Andrew Zisserman
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Qiming Zhang overall
- Jing Zhang
- Di Wang
- Dacheng Tao
- Yunpeng Zhang
- Yufei Xu
- Lukas Hoyer
- Yuxi Wang
- Chenyang Si
- Ke Mei
- Junjue Wang
Papers from Qiming Zhang's perspective
Ordered by how strongly the researchers Qiming Zhang 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias (2021) — 426 citations
- ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation (2022) — 997 citations
- ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond (2022) — 305 citations
- Structured Probabilistic Pruning for Deep Convolutional Neural Network Acceleration (2017) — 11 citations
- Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model (2022) — 379 citations
- Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation (2019) — 312 citations
- Structured Probabilistic Pruning for Convolutional Neural Network Acceleration (2017) — 65 citations