Mingkui Tan

Computer Vision and Pattern Recognition · 2014–2023

A researcher in the Science corpus: 29 papers; trusts 2,166 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 970 citations into it; #6,468 of 21,575 by trust here, #2,040 by citations
• Artificial Intelligence — 713 citations into it; #11,039 of 35,345 by trust here, #2,936 by citations
• Representation & Self-Supervised Learning — 144 citations into it
• Model Architectures — 143 citations into it
• Optimization & Training — 102 citations into it
• Natural Language Processing — 89 citations into it
• Generative Models — 83 citations into it
• Efficiency & Compression — 81 citations into it

Whom Mingkui Tan trusts overall

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

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

Who trusts Mingkui Tan overall

1. Yong Guo
2. Yang He
3. Weizhe Hua
4. Jian Chen
5. Yehui Tang
6. Shuaicheng Niu
7. Runhao Zeng
8. Jing Liu
9. Breton L. Minnehan
10. Sangwon Jung

Papers from Mingkui Tan's perspective

Ordered by how strongly the researchers Mingkui Tan 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

• Deep High-Resolution Representation Learning for Visual Recognition (2019) — 4,876 citations
• Discrimination-aware Channel Pruning for Deep Neural Networks (2018) — 637 citations
• Visual Grounding Via Accumulated Attention (2020) — 205 citations
• Graph Convolutional Networks for Temporal Action Localization (2019) — 542 citations
• Location-Aware Graph Convolutional Networks for Video Question Answering (2020) — 191 citations
• Cascade Reasoning Network for Text-based Visual Question Answering (2020) — 63 citations
• RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning (2020) — 143 citations
• Domain-Symmetric Networks for Adversarial Domain Adaptation (2019) — 354 citations
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019) — 101 citations
• Towards Accurate Text-based Image Captioning with Content Diversity Exploration (2021) — 73 citations
• Adversarial Learning with Local Coordinate Coding (2018) — 39 citations
• Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search (2020) — 78 citations
• A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data (2015) — 35 citations
• Towards Stable Test-Time Adaptation in Dynamic Wild World (2023) — 531 citations
• Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (2022) — 59 citations
• Efficient Test-Time Model Adaptation without Forgetting (2022) — 647 citations
• Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation (2021) — 212 citations
• Dense Regression Network for Video Grounding (2020) — 339 citations
• Discrimination-Aware Network Pruning for Deep Model Compression (2020) — 169 citations
• Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution (2020) — 319 citations