Jing Yao

Media Technology · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020–2023

A researcher in the Science corpus: 9 papers; trusts 168 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 168 citations into it; #2,030 of 3,621 by trust here, #773 by citations
• Computer Vision and Pattern Recognition — 165 citations into it; #13,471 of 21,575 by trust here, #8,952 by citations
• Artificial Intelligence — 93 citations into it; #30,294 of 35,345 by trust here, #19,873 by citations
• Atmospheric Science — 49 citations into it
• Model Architectures — 33 citations into it
• Ecology — 29 citations into it
• Optimization & Training — 17 citations into it
• Statistics and Probability — 5 citations into it

Whom Jing Yao trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Danfeng Hong
4. Kaiming He
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Christian Szegedy

Who trusts Jing Yao overall

1. Swalpa Kumar Roy
2. Danfeng Hong
3. Yapeng Li
4. Xin Wu

Papers from Jing Yao's perspective

Ordered by how strongly the researchers Jing Yao 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework (2023) — 328 citations
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks (2023) — 511 citations
• SpectralGPT: Spectral Remote Sensing Foundation Model (2023) — 837 citations
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022) — 692 citations
• Progress and Challenges in Intelligent Remote Sensing Satellite Systems (2022) — 365 citations
• SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers (2021) — 1,401 citations
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021) — 366 citations
• More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (2020) — 1,193 citations
• Graph Convolutional Networks for Hyperspectral Image Classification (2020) — 1,248 citations