Yang Cao

Computer Vision and Pattern Recognition · International Joint Conference on Artificial Intelligence · 2013–2022

A researcher in the Science corpus: 15 papers; trusts 1,021 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 264 citations into it; #5,921 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 138 citations into it; #27,397 of 35,345 by trust here, #15,236 by citations
• Meta & Continual Learning — 39 citations into it; #849 of 1,041 by trust here, #649 by citations
• Cognitive Neuroscience — 38 citations into it; #4,315 of 10,852 by trust here, #3,702 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Model Architectures — 26 citations into it
• Information Systems — 24 citations into it
• Computational Theory and Mathematics — 21 citations into it

Whom Yang Cao trusts overall

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

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

Who trusts Yang Cao overall

1. Deng-Ping Fan
2. Xiaoqi Zhao
3. Ge-Peng Ji
4. Da-Wei Zhou
5. Jing Zhang
6. Keren Fu
7. Youwei Pang
8. Liyuan Wang
9. Ming-Ming Cheng
10. Chengwei Qin

Papers from Yang Cao's perspective

Ordered by how strongly the researchers Yang Cao 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• EGNet: Edge Guidance Network for Salient Object Detection (2019) — 1,078 citations
• Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection (2019) — 389 citations
• Enhanced-alignment Measure for Binary Foreground Map Evaluation (2018) — 1,626 citations
• Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (2021) — 188 citations
• Self-Supervised Tuning for Few-Shot Segmentation (2020) — 30 citations
• Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (2022) — 198 citations
• A federated graph neural network framework for privacy-preserving personalization (2021) — 328 citations
• Deep Structure-Revealed Network for Texture Recognition (2020) — 42 citations
• Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios (2020) — 428 citations
• Light‐Boosting Highly Sensitive Pressure Sensors Based on Bioinspired Multiscale Surface Structures (2020) — 132 citations
• Detection of Malicious Code Variants Based on Deep Learning (2018) — 514 citations
• Semantic Edge Detection with Diverse Deep Supervision (2018) — 112 citations
• A deep CNN method for underwater image enhancement (2017) — 308 citations
• A simplified HSS preconditioner for generalized saddle point problems (2016) — 67 citations
• A Relaxed HSS Preconditioner for Saddle Point Problems from Meshfree Discretization (2013) — 85 citations