Baochang Zhang

Computer Vision and Pattern Recognition · 2007–2023

A researcher in the Science corpus: 11 papers; trusts 918 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 324 citations into it; #6,705 of 21,575 by trust here, #5,620 by citations
• Artificial Intelligence — 171 citations into it; #16,680 of 35,345 by trust here, #15,236 by citations
• Efficiency & Compression — 80 citations into it
• Model Architectures — 46 citations into it
• Media Technology — 31 citations into it; #1,456 of 3,621 by trust here, #1,859 by citations
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Signal Processing — 14 citations into it

Whom Baochang Zhang trusts overall

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

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

Who trusts Baochang Zhang overall

1. Jiwen Lu
2. Anne Humeau-Heurtier
3. Adín Ramírez Rivera
4. Li Liu
5. Yanfei Zhong
6. Longhui Huang
7. Wei Li
8. Zhen Lei
9. Yap-Peng Tan
10. Jorge A. Rojas Castillo

Papers from Baochang Zhang's perspective

Ordered by how strongly the researchers Baochang 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. 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. Going deeper with convolutions

Their papers

• Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition (2007) — 647 citations
• Towards Optimal Structured CNN Pruning via Generative Adversarial Learning (2019) — 578 citations
• Implicit Diffusion Models for Continuous Super-Resolution (2023) — 383 citations
• Anti-Retroactive Interference for Lifelong Learning (2022) — 30 citations
• HRank: Filter Pruning Using High-Rank Feature Map (2020) — 899 citations
• Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (2019) — 354 citations
• Multinomial Distribution Learning for Effective Neural Architecture Search (2019) — 100 citations
• Accelerating Convolutional Networks via Global & Dynamic Filter Pruning (2018) — 271 citations
• Gabor Convolutional Networks (2017) — 395 citations
• Land-use scene classification using multi-scale completed local binary patterns (2016) — 152 citations
• Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor (2010) — 1,104 citations