Xiaojun Chang

Computer Vision and Pattern Recognition · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2015–2021

A researcher in the Science corpus: 23 papers; trusts 1,706 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 718 citations into it; #8,245 of 21,575 by trust here, #4,317 by citations
• Artificial Intelligence — 683 citations into it; #18,039 of 35,345 by trust here, #6,569 by citations
• Model Architectures — 138 citations into it
• Neural Architecture Search & AutoML — 130 citations into it
• Natural Language Processing — 97 citations into it
• Representation & Self-Supervised Learning — 83 citations into it
• Optimization & Training — 79 citations into it
• Efficiency & Compression — 77 citations into it

Whom Xiaojun Chang trusts overall

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

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

Who trusts Xiaojun Chang overall

1. Brij Bhooshan Gupta
2. Miao Zhang
3. Changlin Li
4. Megha Quamara
5. Pengzhen Ren
6. Xin Ye

Papers from Xiaojun Chang's perspective

Ordered by how strongly the researchers Xiaojun Chang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks (2019) — 286 citations
• BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search (2021) — 124 citations
• Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting (2020) — 57 citations
• Vision-Dialog Navigation by Exploring Cross-Modal Memory (2020) — 54 citations
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021) — 85 citations
• Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016) — 326 citations
• Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation (2019) — 198 citations
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020) — 2,163 citations
• Dynamic Slimmable Network (2021) — 186 citations
• A Comprehensive Survey of Scene Graphs: Generation and Application (2021) — 341 citations
• A Comprehensive Survey of Neural Architecture Search (2020) — 868 citations
• One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (2020) — 81 citations
• A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020) — 300 citations
• A Survey of Deep Active Learning (2020) — 1,521 citations
• Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization (2020) — 89 citations
• Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020) — 326 citations
• Hierarchical Neural Architecture Search for Deep Stereo Matching (2020) — 429 citations
• Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction (2019) — 3 citations
• An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition (2018) — 302 citations
• Teaching Semi-Supervised Classifier via Generalized Distillation (2018) — 28 citations