Jing-Hao Xue

Computer Vision and Pattern Recognition · with Yang · 2008–2021

A researcher in the Science corpus: 13 papers; trusts 1,733 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 661 citations into it; #7,783 of 21,575 by trust here, #5,375 by citations
• Generative Models — 305 citations into it; #1,104 of 1,956 by trust here, #899 by citations
• Artificial Intelligence — 173 citations into it
• Media Technology — 85 citations into it; #3,013 of 3,621 by trust here, #1,696 by citations
• Multimodal Learning — 56 citations into it
• Representation & Self-Supervised Learning — 52 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Computer Graphics and Computer-Aided Design — 40 citations into it

Whom Jing-Hao Xue trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Karen Simonyan
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Jing-Hao Xue overall

1. Weihao Xia
2. Yujiu Yang
3. Yufan Zhou
4. Zhu Zhang
5. Hao Wang
6. Jianqing Fan
7. Yulun Zhang

Papers from Jing-Hao Xue's perspective

Ordered by how strongly the researchers Jing-Hao Xue trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation (2021) — 465 citations
• GAN Inversion: A Survey (2021) — 638 citations
• Deep Learning for Single Image Super-Resolution: A Brief Review (2018) — 1,052 citations
• Depth-images-based pose estimation using regression forests and graphical models (2015) — 27 citations
• TediGAN: Text-Guided Diverse Image Generation and Manipulation (2020) — 24 citations
• Domain Fingerprints for No-Reference Image Quality Assessment (2019) — 14 citations
• Controllable Continuous Gaze Redirection (2020) — 18 citations
• A novel hierarchical framework for human action recognition (2016) — 71 citations
• Towards Open-World Text-Guided Face Image Generation and Manipulation (2021) — 49 citations
• IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection (2020) — 76 citations
• Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions (2019) — 13 citations
• Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches (2019) — 15 citations
• Sure independence screening for ultrahigh dimensional feature space Discussion (2008) — 358 citations