Zi-Liang Huang

Computer Vision and Pattern Recognition · 2011–2022

A researcher in the Science corpus: 13 papers; trusts 826 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 218 citations into it; #5,494 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 214 citations into it; #21,652 of 35,345 by trust here, #12,387 by citations
• Representation & Self-Supervised Learning — 91 citations into it
• Information Systems — 70 citations into it; #4,732 of 8,771 by trust here, #3,761 by citations
• Natural Language Processing — 30 citations into it
• Model Architectures — 26 citations into it
• Management Science and Operations Research — 23 citations into it
• Information Retrieval & Search — 18 citations into it

Whom Zi-Liang Huang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yann LeCun
8. Ruslan Salakhutdinov
9. David G. Lowe
10. Ross B. Girshick

Who trusts Zi-Liang Huang overall

1. Mingsheng Long
2. Jingkuan Song
3. Kaiye Wang
4. Jingdong Wang
5. Junliang Yu
6. Miaomiao Cheng
7. Dongqing Zhang
8. Zhou Yu
9. Bokun Wang
10. Jianmin Wang

Papers from Zi-Liang Huang's perspective

Ordered by how strongly the researchers Zi-Liang Huang 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Multi-attention Network for One Shot Learning (2017) — 86 citations
• Multiple feature hashing for real-time large scale near-duplicate video retrieval (2011) — 327 citations
• Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013) — 616 citations
• Linear cross-modal hashing for efficient multimedia search (2013) — 297 citations
• Leveraging the Invariant Side of Generative Zero-Shot Learning (2019) — 338 citations
• Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018) — 372 citations
• Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2019) — 228 citations
• Self-Supervised Learning for Recommender Systems: A Survey (2022) — 438 citations
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (2021) — 572 citations
• Learning to Diversify for Single Domain Generalization (2021) — 353 citations
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019) — 417 citations
• Leveraging Weak Semantic Relevance for Complex Video Event Classification (2017) — 10 citations
• - Norm Regularized Discriminative Feature Selection for Unsupervised Learning (2011)