Ping Wang

Computer Vision and Pattern Recognition · AAAI Conference on Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 817 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 294 citations into it; #11,708 of 21,575 by trust here, #6,426 by citations
• Artificial Intelligence — 213 citations into it; #18,708 of 35,345 by trust here, #9,092 by citations
• Model Architectures — 43 citations into it
• Efficiency & Compression — 32 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Graph Learning — 19 citations into it
• Optimization & Training — 19 citations into it

Whom Ping Wang trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Shaoqing Ren
8. Joseph Redmon
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Ping Wang overall

1. Siliang Ma
2. Juan R. Terven
3. Chien-Yao Wang
4. Zhaohui Zheng
5. Xue Yang
6. Alexey Bochkovskiy
7. Ajantha Vijayakumar
8. Raja Sunkara
9. Yong Xu
10. Diana-Margarita Córdova-Esparza

Papers from Ping Wang's perspective

Ordered by how strongly the researchers Ping Wang 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. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (2019) — 5,129 citations
• Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks (2019) — 295 citations
• Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning (2021) — 36 citations
• Localization Distillation for Object Detection (2022) — 94 citations
• A survey on deep learning and its applications (2021) — 1,346 citations
• Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation (2020) — 1,369 citations