Yanwei Pang

Computer Vision and Pattern Recognition · 2008–2020

A researcher in the Science corpus: 15 papers; trusts 1,305 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 391 citations into it; #8,055 of 21,575 by trust here, #5,333 by citations
• Artificial Intelligence — 156 citations into it; #19,357 of 35,345 by trust here, #12,387 by citations
• Model Architectures — 41 citations into it
• Media Technology — 34 citations into it; #2,646 of 3,621 by trust here, #2,954 by citations
• Signal Processing — 24 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Natural Language Processing — 10 citations into it
• Neurology — 7 citations into it

Whom Yanwei Pang trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Navneet Dalal
4. Alex Krizhevsky
5. Karen Simonyan
6. Geoffrey E. Hinton
7. Tsung-Yi Lin
8. Shaoqing Ren
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Yanwei Pang overall

1. Mehrtash Harandi
2. Jacopo Cavazza
3. Rao Muhammad Anwer
4. Lei Wang
5. Tiancai Wang
6. Jing Nie
7. Sung-Jin Cho
8. Hisham Cholakkal
9. Jiale Cao
10. Fahad Shahbaz Khan

Papers from Yanwei Pang's perspective

Ordered by how strongly the researchers Yanwei Pang 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (2020) — 195 citations
• Triply Supervised Decoder Networks for Joint Detection and Segmentation (2018) — 68 citations
• Learning Rich Features at High-Speed for Single-Shot Object Detection (2019) — 97 citations
• Enriched Feature Guided Refinement Network for Object Detection (2019) — 81 citations
• Efficient Featurized Image Pyramid Network for Single Shot Detector (2019) — 106 citations
• Learning Multi-Level Density Maps for Crowd Counting (2020) — 59 citations
• D2Det: Towards High Quality Object Detection and Instance Segmentation (2020) — 278 citations
• Deep Attentive Video Summarization With Distribution Consistency Learning (2020) — 83 citations
• Attention Scaling for Crowd Counting (2020) — 307 citations
• LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement (2018) — 484 citations
• Video Summarization With Attention-Based Encoder–Decoder Networks (2017) — 373 citations
• Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016) — 813 citations
• Efficient HOG human detection (2011) — 347 citations
• Photo2Trip: generating travel routes from geo-tagged photos for trip planning (2010) — 312 citations
• Gabor-Based Region Covariance Matrices for Face Recognition (2008) — 299 citations