Chang Xu

Computer Vision and Pattern Recognition · arXiv.org · 2005–2022

A researcher in the Science corpus: 23 papers; trusts 1,752 researchers and is trusted by 100 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 567 citations into it; #3,266 of 21,575 by trust here, #2,783 by citations
• Artificial Intelligence — 377 citations into it; #5,664 of 35,345 by trust here, #4,722 by citations
• Efficiency & Compression — 121 citations into it
• Model Architectures — 92 citations into it
• Generative Models — 90 citations into it
• Representation & Self-Supervised Learning — 62 citations into it; #1,118 of 3,145 by trust here, #1,109 by citations
• Optimization & Training — 53 citations into it
• Evaluation & Benchmarks — 47 citations into it

Whom Chang Xu trusts overall

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

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

Who trusts Chang Xu overall

1. Dacheng Tao
2. Yunhe Wang
3. Amir Zadeh
4. Shan You
5. Gabriella Csurka
6. Changxing Ding
7. Maria Brbic
8. Percy Liang
9. Chao Xu
10. Changqing Zhang

Papers from Chang Xu's perspective

Ordered by how strongly the researchers Chang Xu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Survey on Multi-view Learning (2013) — 1,241 citations
• Multi-View Intact Space Learning (2015) — 385 citations
• Self-Supervised Representation Learning by Rotation Feature Decoupling (2019) — 215 citations
• CMT: Convolutional Neural Networks Meet Vision Transformers (2021) — 951 citations
• Image-Question-Answer Synergistic Network for Visual Dialog (2019) — 83 citations
• Perceptual Adversarial Networks for Image-to-Image Transformation (2017) — 392 citations
• Large-Margin Multi-ViewInformation Bottleneck (2014) — 258 citations
• Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark (2022) — 369 citations
• GhostNetV2: Enhance Cheap Operation with Long-Range Attention (2022) — 579 citations
• A Normalized Gaussian Wasserstein Distance for Tiny Object Detection (2021) — 595 citations
• Distilling portable Generative Adversarial Networks for Image Translation (2020) — 89 citations
• Bringing Giant Neural Networks Down to Earth with Unlabeled Data (2019) — 11 citations
• Entropy production diagnostic analysis of energy consumption for cavitation flow in a two-stage LNG cryogenic submerged pump (2019) — 154 citations
• Towards Evolutionary Compression (2018) — 45 citations
• Learning Student Networks via Feature Embedding (2018) — 116 citations
• Evolutionary Generative Adversarial Networks (2018) — 329 citations
• Learning With Single-Teacher Multi-Student (2018) — 29 citations
• Adversarial Learning of Portable Student Networks (2018) — 76 citations
• Experimental and theoretical evaluation on the thermal performance of a windowed volumetric solar receiver (2017) — 142 citations
• CNNpack: Packing Convolutional Neural Networks in the Frequency Domain (2016) — 197 citations