Huaizu Jiang

Computer Vision and Pattern Recognition · 2011–2020

A researcher in the Science corpus: 9 papers; trusts 1,563 researchers and is trusted by 288 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 472 citations into it; #2,248 of 21,575 by trust here, #1,847 by citations
• Artificial Intelligence — 126 citations into it; #3,117 of 35,345 by trust here, #3,146 by citations
• Cognitive Neuroscience — 78 citations into it; #2,466 of 10,852 by trust here, #2,700 by citations
• Sensory Systems — 49 citations into it
• Evaluation & Benchmarks — 34 citations into it; #745 of 3,147 by trust here, #1,171 by citations
• Media Technology — 24 citations into it
• Human-Computer Interaction — 22 citations into it
• Model Architectures — 21 citations into it

Whom Huaizu Jiang trusts overall

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

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

Who trusts Huaizu Jiang overall

1. Ming-Ming Cheng
2. Deng-Ping Fan
3. Huchuan Lu
4. Yancheng Bai
5. Guanbin Li
6. Ali Borji
7. Houwen Peng
8. Yongqiang Zhang
9. Qibin Hou
10. Junwei Han

Papers from Huaizu Jiang's perspective

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

Their papers

• Salient Object Detection: A Benchmark (2015) — 1,834 citations
• In Defense of Grid Features for Visual Question Answering (2020) — 368 citations
• Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013) — 1,301 citations
• Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation (2017) — 909 citations
• Face Detection with the Faster R-CNN (2016) — 732 citations
• Unsupervised Hard Example Mining from Videos for Improved Object Detection (2018) — 69 citations
• Automatic Adaptation of Object Detectors to New Domains Using Self-Training (2019) — 184 citations
• Automatic salient object segmentation based on context and shape prior (2011) — 496 citations
• Salient object detection: A survey (2014) — 836 citations