Zhiwu Huang

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 9 papers; trusts 1,045 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 260 citations into it; #4,785 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 167 citations into it; #13,758 of 35,345 by trust here, #16,771 by citations
• Meta & Continual Learning — 65 citations into it; #510 of 1,041 by trust here, #897 by citations
• Generative Models — 62 citations into it; #1,710 of 1,956 by trust here, #1,715 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Model Architectures — 34 citations into it
• Optimization & Training — 30 citations into it
• Computational Mechanics — 26 citations into it; #7,899 of 10,001 by trust here, #9,276 by citations

Whom Zhiwu Huang trusts overall

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

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

Who trusts Zhiwu Huang overall

1. Yongming Rao
2. Luc Van Gool
3. Jacopo Cavazza
4. Jiqing Wu
5. Chengde Wan
6. Ce Ju
7. Jiwen Lu
8. Da-Wei Zhou
9. Chaolong Li
10. Hossein Rahmani

Papers from Zhiwu Huang's perspective

Ordered by how strongly the researchers Zhiwu Huang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs (2018) — 74 citations
• Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition (2015) — 250 citations
• S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning (2022) — 371 citations
• A Riemannian Network for SPD Matrix Learning (2016) — 523 citations
• Wasserstein Divergence for GANs (2017) — 163 citations
• Building Deep Networks on Grassmann Manifolds (2016) — 198 citations
• Deep Learning on Lie Groups for Skeleton-Based Action Recognition (2016) — 296 citations
• A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials (2022) — 87 citations
• Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (2015) — 264 citations