Jingkang Yang

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 10 papers; trusts 937 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 487 citations into it; #6,582 of 35,345 by trust here, #3,192 by citations
• Computer Vision and Pattern Recognition — 401 citations into it; #4,971 of 21,575 by trust here, #3,280 by citations
• Multimodal Learning — 124 citations into it; #813 of 1,211 by trust here, #364 by citations
• Large Language Models — 84 citations into it
• Evaluation & Benchmarks — 83 citations into it
• Representation & Self-Supervised Learning — 75 citations into it
• Safety, Robustness & Interpretability — 58 citations into it
• Natural Language Processing — 35 citations into it

Whom Jingkang Yang trusts overall

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

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

Who trusts Jingkang Yang overall

1. Renrui Zhang
2. Gyuhak Kim
3. Kaiyang Zhou
4. Muhammad Uzair Khattak
5. Haoqi Wang
6. Yabin Wang
7. Siyuan Li
8. Junting Pan
9. Qihang Zhou
10. Qiang Zhou

Papers from Jingkang Yang's perspective

Ordered by how strongly the researchers Jingkang Yang 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning to Prompt for Vision-Language Models (2021) — 4,174 citations
• MIMIC-IT: Multi-Modal In-Context Instruction Tuning (2023) — 310 citations
• Conditional Prompt Learning for Vision-Language Models (2022) — 2,313 citations
• Generalized Out-of-Distribution Detection: A Survey (2021) — 1,452 citations
• Otter: A Multi-Modal Model With In-Context Instruction Tuning (2023) — 692 citations
• LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2024) — 331 citations
• Long Context Transfer from Language to Vision (2024) — 531 citations
• OtterHD: A High-Resolution Multi-modality Model (2023) — 87 citations
• OpenOOD: Benchmarking Generalized Out-of-Distribution Detection (2022) — 381 citations
• Semantically Coherent Out-of-Distribution Detection (2021) — 160 citations