Xiang Ruan

Computer Vision and Pattern Recognition · 2013–2018

A researcher in the Science corpus: 8 papers; trusts 996 researchers and is trusted by 223 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 242 citations into it; #2,282 of 21,575 by trust here, #1,641 by citations
• Cognitive Neuroscience — 51 citations into it; #728 of 10,852 by trust here, #1,312 by citations
• Artificial Intelligence — 40 citations into it
• Sensory Systems — 37 citations into it; #8 of 760 by trust here, #17 by citations
• Model Architectures — 14 citations into it
• Human-Computer Interaction — 10 citations into it
• Media Technology — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Xiang Ruan trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Ming-Ming Cheng
6. Karen Simonyan
7. Yann LeCun
8. Yoshua Bengio
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Xiang Ruan overall

1. Wenguan Wang
2. Huchuan Lu
3. Tiantian Wang
4. Pingping Zhang
5. Zhe Wu
6. Deng-Ping Fan
7. Junwei Han
8. Ali Borji
9. Guanbin Li
10. Nian Liu

Papers from Xiang Ruan's perspective

Ordered by how strongly the researchers Xiang Ruan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Saliency Detection via Graph-Based Manifold Ranking (2013) — 2,484 citations
• Deep networks for saliency detection via local estimation and global search (2015) — 650 citations
• Saliency Detection via Dense and Sparse Reconstruction (2013) — 708 citations
• Learning to Detect Salient Objects with Image-Level Supervision (2017) — 1,257 citations
• Saliency Detection with Recurrent Fully Convolutional Networks (2016) — 532 citations
• Detect Globally, Refine Locally: A Novel Approach to Saliency Detection (2018) — 391 citations
• Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017) — 790 citations
• Salient object detection via bootstrap learning (2015) — 307 citations