Jian Ding
Computer Vision and Pattern Recognition · 2017–2021
A researcher in the Science corpus: 7 papers; trusts 1,005 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 270 citations into it; #4,760 of 21,575 by trust here, #3,558 by citations
- Artificial Intelligence — 106 citations into it
- Media Technology — 39 citations into it; #2,531 of 3,621 by trust here, #505 by citations
- Evaluation & Benchmarks — 34 citations into it; #2,349 of 3,147 by trust here, #1,262 by citations
- Aerospace Engineering — 28 citations into it; #2,038 of 6,421 by trust here, #1,934 by citations
- Model Architectures — 25 citations into it
- Optimization & Training — 7 citations into it
- Ocean Engineering — 6 citations into it
Whom Jian Ding trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Kaiming He
- Tsung-Yi Lin
- Geoffrey E. Hinton
- Alex Krizhevsky
- Shaoqing Ren
- Karen Simonyan
- Diederik P. Kingma
- Yoshua Bengio
- Piotr Doll'ar
Who trusts Jian Ding overall
- Xue Yang
- Jiaming Han
- Gong Cheng
- Xinhao Cai
- Junchi Yan
- Wentong Li
- Yiming Sun
- Di Wang
- Thorsten Hoeser
- Qi Ming
Papers from Jian Ding's perspective
Ordered by how strongly the researchers Jian Ding 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)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (2017) — 2,943 citations
- Decoupling Zero-Shot Semantic Segmentation (2021) — 305 citations
- DetCo: Unsupervised Contrastive Learning for Object Detection (2021) — 369 citations
- Learning RoI Transformer for Oriented Object Detection in Aerial Images (2019) — 1,156 citations
- Align Deep Features for Oriented Object Detection (2020) — 1,122 citations
- ReDet: A Rotation-equivariant Detector for Aerial Object Detection (2021) — 830 citations
- Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges (2021) — 655 citations