Zhenbao Liu

Computer Vision and Pattern Recognition · 2012–2019

A researcher in the Science corpus: 8 papers; trusts 424 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 158 citations into it; #6,111 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 57 citations into it
• Media Technology — 42 citations into it; #1,002 of 3,621 by trust here, #594 by citations
• Computational Mechanics — 29 citations into it
• Aerospace Engineering — 28 citations into it; #5,578 of 6,421 by trust here, #3,911 by citations
• Remote Sensing — 19 citations into it; #52 of 95 by trust here, #26 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Model Architectures — 9 citations into it

Whom Zhenbao Liu trusts overall

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

1. Geoffrey E. Hinton
2. David G. Lowe
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ross B. Girshick
6. Kaiming He
7. Yann LeCun
8. Ilya Sutskever
9. Andrew Zisserman
10. Junwei Han

Who trusts Zhenbao Liu overall

1. Gong Cheng
2. Peicheng Zhou
3. Junwei Han
4. Xiwen Yao
5. Dingwen Zhang
6. Nassim Ammour
7. Souleyman Chaib

Papers from Zhenbao Liu's perspective

Ordered by how strongly the researchers Zhenbao Liu 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. 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

• 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation (2019) — 180 citations
• Scene parsing using inference Embedded Deep Networks (2016) — 22 citations
• A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF (2015) — 87 citations
• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015) — 91 citations
• Weakly Supervised Learning for Target Detection in Remote Sensing Images (2015) — 99 citations
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014) — 215 citations
• Square-root quaternion cubature Kalman filtering for spacecraft attitude estimation (2012) — 70 citations
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M