Mingyu Ding

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 8 papers; trusts 901 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 349 citations into it
• Artificial Intelligence — 285 citations into it
• Representation & Self-Supervised Learning — 100 citations into it
• Model Architectures — 72 citations into it
• Multimodal Learning — 40 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Natural Language Processing — 32 citations into it
• Optimization & Training — 24 citations into it

Whom Mingyu Ding trusts overall

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

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

Who trusts Mingyu Ding overall

1. Chien-Yao Wang
2. Yunpeng Zhang
3. Cody Reading
4. Xueyan Zou
5. Jianwei Yang
6. Xinzhu Ma
7. Dennis Park
8. Yue Liu
9. Jianfeng Wang
10. Bin Xiao

Papers from Mingyu Ding's perspective

Ordered by how strongly the researchers Mingyu Ding 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. ImageNet: A large-scale hierarchical image database
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

• EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought (2023) — 430 citations
• Context Autoencoder for Self-supervised Representation Learning (2022) — 508 citations
• DaViT: Dual Attention Vision Transformers (2022) — 406 citations
• Learning Depth-Guided Convolutions for Monocular 3D Object Detection (2019) — 372 citations
• Planning with Large Language Models for Code Generation (2023) — 256 citations
• Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language (2021) — 86 citations
• Domain-Adaptive Few-Shot Learning (2020) — 93 citations
• Domain-Invariant Projection Learning for Zero-Shot Recognition (2018) — 50 citations