Bing Zhang

Computer Vision and Pattern Recognition · IEEE Transactions on Geoscience and Remote Sensing · 2018–2023

A researcher in the Science corpus: 8 papers; trusts 156 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it; #12,949 of 21,575 by trust here, #9,305 by citations
• Media Technology — 114 citations into it; #1,824 of 3,621 by trust here, #648 by citations
• Artificial Intelligence — 79 citations into it; #30,293 of 35,345 by trust here, #20,836 by citations
• Atmospheric Science — 35 citations into it
• Model Architectures — 29 citations into it
• Ecology — 24 citations into it; #10,683 of 10,717 by trust here, #5,442 by citations
• Optimization & Training — 14 citations into it

Whom Bing Zhang trusts overall

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

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

Who trusts Bing Zhang overall

1. Danfeng Hong

Papers from Bing Zhang's perspective

Ordered by how strongly the researchers Bing Zhang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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
• SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers (2021) — 1,401 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
• Multisource Remote Sensing Data Classification Based on Convolutional Neural Network (2018) — 614 citations