Qiang Qiu

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 11 papers; trusts 1,072 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 265 citations into it; #8,989 of 21,575 by trust here, #6,795 by citations
• Artificial Intelligence — 162 citations into it; #7,978 of 35,345 by trust here, #11,624 by citations
• Representation & Self-Supervised Learning — 68 citations into it
• Model Architectures — 43 citations into it; #1,201 of 2,278 by trust here, #1,300 by citations
• Computational Mechanics — 30 citations into it; #5,178 of 10,001 by trust here, #5,157 by citations
• Signal Processing — 30 citations into it; #2,070 of 7,572 by trust here, #3,198 by citations
• Meta & Continual Learning — 26 citations into it
• Generative Models — 23 citations into it

Whom Qiang Qiu trusts overall

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

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

Who trusts Qiang Qiu overall

1. Gabriella Csurka
2. Vishal M. Patel
3. He Zhang
4. Xiuyuan Cheng
5. Sumit Shekhar
6. Ramalingam Chellappa
7. Hien Van Nguyen
8. Atsutoshi Kumagai
9. Ze Wang
10. Tatiana Tommasi

Papers from Qiang Qiu's perspective

Ordered by how strongly the researchers Qiang Qiu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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

• Oriented Response Networks (2017) — 283 citations
• Weakly Supervised Instance Segmentation Using Class Peak Response (2018) — 294 citations
• Domain Adaptive Dictionary Learning (2012) — 152 citations
• RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks (2018) — 47 citations
• Continual Learning with Filter Atom Swapping (2022) — 36 citations
• Adversarially Learned Representations for Information Obfuscation and Inference (2019) — 60 citations
• Stochastic Conditional Generative Networks with Basis Decomposition (2019) — 19 citations
• DCFNet: Deep Neural Network with Decomposed Convolutional Filters (2018) — 73 citations
• OLE: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning (2017) — 88 citations
• Discriminative Robust Transformation Learning (2015) — 9 citations
• Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation (2013) — 195 citations