Xinghao Ding
Computer Vision and Pattern Recognition · 2014–2023
A researcher in the Science corpus: 13 papers; trusts 807 researchers and is trusted by 50 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 264 citations into it; #3,985 of 21,575 by trust here, #2,902 by citations
- Artificial Intelligence — 96 citations into it
- Media Technology — 81 citations into it; #1,397 of 3,621 by trust here, #348 by citations
- Representation & Self-Supervised Learning — 44 citations into it
- Model Architectures — 16 citations into it
- Optimization & Training — 16 citations into it
- Atomic and Molecular Physics, and Optics — 14 citations into it; #1,090 of 7,135 by trust here, #2,263 by citations
- Evaluation & Benchmarks — 14 citations into it
Whom Xinghao Ding trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ross B. Girshick
- Yann LeCun
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Karen Simonyan
- Shaoqing Ren
Who trusts Xinghao Ding overall
- Wenhan Yang
- He Zhang
- Chris H. Bahnsen
- Vishal M. Patel
- Jiaying Liu
- Syed Waqas Zamir
- Ruoteng Li
- Ozan Özdenizci
- Chongyi Li
- Wei Wei
Papers from Xinghao Ding's perspective
Ordered by how strongly the researchers Xinghao 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation (2016) — 1,030 citations
- Progressive Feature Alignment for Unsupervised Domain Adaptation (2018) — 481 citations
- Removing Rain from Single Images via a Deep Detail Network (2017) — 1,228 citations
- Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal (2016) — 906 citations
- A fusion-based enhancing method for weakly illuminated images (2016) — 681 citations
- Lightweight Pyramid Networks for Image Deraining (2018) — 396 citations
- Harmonizing Transferability and Discriminability for Adapting Object Detectors (2020) — 335 citations
- Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023) — 348 citations
- Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes (2019) — 256 citations
- PanNet: A Deep Network Architecture for Pan-Sharpening (2017) — 758 citations
- A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation (2015) — 414 citations
- A retinex-based enhancing approach for single underwater image (2014) — 563 citations
- Multiple-instance discriminant analysis (2014) — 8 citations