Yu Liu

Computer Vision and Pattern Recognition · 2017–2024

A researcher in the Science corpus: 18 papers; trusts 1,913 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 642 citations into it; #8,165 of 21,575 by trust here, #3,558 by citations
• Artificial Intelligence — 376 citations into it; #19,585 of 35,345 by trust here, #4,956 by citations
• Model Architectures — 135 citations into it
• Efficiency & Compression — 68 citations into it
• Optimization & Training — 65 citations into it
• Signal Processing — 60 citations into it; #5,362 of 7,572 by trust here, #2,343 by citations
• Evaluation & Benchmarks — 50 citations into it
• Multimodal Learning — 45 citations into it

Whom Yu Liu trusts overall

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

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

Who trusts Yu Liu overall

1. Mang Ye
2. Manyuan Zhang
3. Guanglu Song
4. Jianping Gou
5. Zhongdao Wang
6. Yuehua Wu
7. Jianlou Si
8. Lele Chen
9. Hang Zhou
10. Linfeng Zhang

Papers from Yu Liu's perspective

Ordered by how strongly the researchers Yu 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

• Correlation Congruence for Knowledge Distillation (2019) — 645 citations
• Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (2018) — 490 citations
• Revisiting the Sibling Head in Object Detector (2020) — 415 citations
• Quality Aware Network for Set to Set Recognition (2017) — 327 citations
• Knowledge Distillation via Route Constrained Optimization (2019) — 203 citations
• Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection (2017) — 82 citations
• UniFormer: Unifying Convolution and Self-Attention for Visual Recognition (2022) — 610 citations
• UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning (2022) — 368 citations
• Conditional Adversarial Generative Flow for Controllable Image Synthesis (2019) — 48 citations
• Scale-Aware Face Detection (2017) — 116 citations
• Meta Knowledge Distillation (2022) — 35 citations
• Gradient Harmonized Single-stage Detector (2018) — 597 citations
• Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning (2024) — 348 citations
• LMDrive: Closed-Loop End-to-End Driving with Large Language Models (2023) — 378 citations
• DETRs with Collaborative Hybrid Assignments Training (2022) — 644 citations
• Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer (2022) — 366 citations
• Discriminability Distillation in Group Representation Learning (2020) — 20 citations
• Search to Distill: Pearls Are Everywhere but Not the Eyes (2019) — 73 citations