Wenyu Liu

Computer Vision and Pattern Recognition · 2018–2024

A researcher in the Science corpus: 23 papers; trusts 1,934 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 1,137 citations into it; #8,009 of 21,575 by trust here, #1,964 by citations
• Artificial Intelligence — 527 citations into it; #30,570 of 35,345 by trust here, #4,466 by citations
• Model Architectures — 169 citations into it
• Computer Graphics and Computer-Aided Design — 96 citations into it
• Evaluation & Benchmarks — 95 citations into it
• Optimization & Training — 67 citations into it
• Aerospace Engineering — 57 citations into it
• Efficiency & Compression — 50 citations into it

Whom Wenyu Liu trusts overall

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

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

Who trusts Wenyu Liu overall

1. Xinggang Wang
2. Jingdong Wang
3. Kris M. Kitani
4. Luc Van Gool
5. Jinkun Cao
6. Qian Zhang
7. Ping Luo
8. Yuhui Yuan
9. Xinchao Wang
10. Song Bai

Papers from Wenyu Liu's perspective

Ordered by how strongly the researchers Wenyu Liu 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep High-Resolution Representation Learning for Visual Recognition (2019) — 4,876 citations
• High-Resolution Representations for Labeling Pixels and Regions (2019) — 909 citations
• Instances as Queries (2021) — 347 citations
• You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection (2021) — 416 citations
• FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking (2020) — 1,833 citations
• Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (2022) — 463 citations
• ByteTrack: Multi-Object Tracking by Associating Every Detection Box (2021) — 2,498 citations
• Crossover Learning for Fast Online Video Instance Segmentation (2021) — 127 citations
• 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering (2023) — 1,495 citations
• TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation (2022) — 301 citations
• A PyTorch Semantic Segmentation Toolbox (2018)
• The Seventh Visual Object Tracking VOT2019 Challenge Results (2019) — 486 citations
• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (2024) — 1,963 citations
• YOLO-World: Real-Time Open-Vocabulary Object Detection (2024) — 985 citations
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning (2024) — 298 citations
• GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models (2023) — 319 citations
• VAD: Vectorized Scene Representation for Efficient Autonomous Driving (2023) — 729 citations
• MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction (2022) — 416 citations
• Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation (2021) — 13 citations
• ResizeMix: Mixing Data with Preserved Object Information and True Labels (2020) — 105 citations