Jian Cheng

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 15 papers; trusts 1,462 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 395 citations into it; #4,191 of 21,575 by trust here, #2,804 by citations
• Artificial Intelligence — 196 citations into it
• Biomedical Engineering — 88 citations into it; #996 of 10,734 by trust here, #3,931 by citations
• Model Architectures — 71 citations into it
• Human-Computer Interaction — 53 citations into it; #825 of 2,253 by trust here, #639 by citations
• Efficiency & Compression — 36 citations into it; #492 of 1,264 by trust here, #337 by citations
• Graph Learning — 26 citations into it
• Representation & Self-Supervised Learning — 22 citations into it

Whom Jian Cheng trusts overall

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

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

Who trusts Jian Cheng overall

1. Yisheng Song
2. Zhan Chen
3. Ke Cheng
4. Liang Wang
5. Chiara Plizzari
6. Zhang Zhang
7. Hongsong Wang
8. Yifan Zhang
9. Mang Ye
10. Jun Liu

Papers from Jian Cheng's perspective

Ordered by how strongly the researchers Jian Cheng 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Quantized Convolutional Neural Networks for Mobile Devices (2015) — 1,245 citations
• Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition (2018) — 1,855 citations
• Fusing multi-modal features for gesture recognition (2013) — 72 citations
• RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment (2019) — 477 citations
• Skeleton-Based Action Recognition With Shift Graph Convolutional Network (2020) — 845 citations
• Skeleton-Based Action Recognition With Directed Graph Neural Networks (2019) — 798 citations
• Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition (2020) — 338 citations
• Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks (2019) — 543 citations
• Towards Accurate Post-training Network Quantization via Bit-Split and Stitching (2020) — 159 citations
• Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification (2020) — 369 citations
• SpatialFlow: Bridging All Tasks for Panoptic Segmentation (2019) — 33 citations
• EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018) — 319 citations
• Accelerating Convolutional Neural Networks for Mobile Applications (2016) — 75 citations
• Hashing for Distributed Data (2015) — 31 citations
• Online sketching hashing (2015) — 126 citations