Li Niu

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 1,017 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 165 citations into it; #5,833 of 21,575 by trust here, #5,013 by citations
• Artificial Intelligence — 111 citations into it; #8,125 of 35,345 by trust here, #7,346 by citations
• Representation & Self-Supervised Learning — 49 citations into it; #1,849 of 3,145 by trust here, #1,781 by citations
• Cancer Research — 17 citations into it
• Generative Models — 16 citations into it
• Multimodal Learning — 16 citations into it
• Media Technology — 14 citations into it
• Natural Language Processing — 14 citations into it

Whom Li Niu trusts overall

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

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

Who trusts Li Niu overall

1. Haohan Wang
2. Jun Shu
3. Zhangxuan Gu
4. Wen Li
5. Amita Kamath
6. Kaiyang Zhou
7. Wenyan Cong
8. Gabriella Csurka
9. Chong Zhou
10. Saeid Motiian

Papers from Li Niu's perspective

Ordered by how strongly the researchers Li Niu 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models (2017) — 385 citations
• Exploiting Low-Rank Structure from Latent Domains for Domain Generalization (2014) — 204 citations
• Context-aware Feature Generation For Zero-shot Semantic Segmentation (2020) — 164 citations
• Multi-view Domain Generalization for Visual Recognition (2015) — 65 citations
• Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification (2018) — 71 citations
• DoveNet: Deep Image Harmonization via Domain Verification (2019) — 261 citations