Zechao Li

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 10 papers; trusts 837 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 168 citations into it; #12,605 of 21,575 by trust here, #7,750 by citations
• Artificial Intelligence — 82 citations into it; #26,749 of 35,345 by trust here, #17,110 by citations
• Representation & Self-Supervised Learning — 49 citations into it
• Media Technology — 23 citations into it; #3,002 of 3,621 by trust here, #1,423 by citations
• Evaluation & Benchmarks — 15 citations into it
• Aerospace Engineering — 11 citations into it
• Optimization & Training — 11 citations into it
• Multimodal Learning — 10 citations into it

Whom Zechao Li trusts overall

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

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

Who trusts Zechao Li overall

1. Jundong Li
2. J. Liu
3. Yue He
4. Yufei Han
5. Saúl Solorio-Fernández
6. Feiping Nie
7. Hamed Jelodar
8. Jianfang Hu
9. Jinhui Tang
10. Yang Liu

Papers from Zechao Li's perspective

Ordered by how strongly the researchers Zechao Li 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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

• Single Image Dehazing via Conditional Generative Adversarial Network (2018) — 405 citations
• Few-Shot Image Recognition With Knowledge Transfer (2019) — 232 citations
• Weakly-Supervised Dual Clustering for Image Semantic Segmentation (2013) — 76 citations
• Learning attention-guided pyramidal features for few-shot fine-grained recognition (2022) — 335 citations
• Deep Collaborative Embedding for Social Image Understanding (2019) — 347 citations
• Robust Structured Subspace Learning for Data Representation (2015) — 359 citations
• Face Clustering in Videos with Proportion Prior (2015) — 8 citations
• Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition (2014) — 47 citations
• Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (2014) — 314 citations
• Unsupervised Feature Selection Using Nonnegative Spectral Analysis (2012) — 541 citations