Shiliang Pu

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 12 papers; trusts 933 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 411 citations into it; #7,076 of 21,575 by trust here, #5,375 by citations
• Artificial Intelligence — 241 citations into it; #22,587 of 35,345 by trust here, #11,490 by citations
• Representation & Self-Supervised Learning — 60 citations into it
• Model Architectures — 51 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Multimodal Learning — 33 citations into it
• Efficiency & Compression — 32 citations into it
• Natural Language Processing — 32 citations into it

Whom Shiliang Pu trusts overall

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

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

Who trusts Shiliang Pu overall

1. Yisheng Song
2. Zhang Zhang
3. Lei Shi
4. Fanfan Ye

Papers from Shiliang Pu's perspective

Ordered by how strongly the researchers Shiliang Pu 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. Long Short-Term Memory
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Image Classification for Deep Representation Learning (2020) — 14 citations
• Skeleton-based action recognition with convolutional neural networks (2017) — 417 citations
• Forward Compatible Few-Shot Class-Incremental Learning (2022) — 288 citations
• Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019) — 337 citations
• Extreme Network Compression via Filter Group Approximation (2018) — 67 citations
• Counterfactual Samples Synthesizing for Robust Visual Question Answering (2020) — 330 citations
• Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition (2020) — 348 citations
• RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation (2021) — 364 citations
• Multichannel Attention Refinement for Video Question Answering (2020) — 31 citations
• Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation (2018) — 579 citations
• A Practical Convolutional Neural Network as Loop Filter for Intra Frame (2018) — 42 citations
• Focusing Attention: Towards Accurate Text Recognition in Natural Images (2017) — 493 citations