Hongyang Li

Computer Vision and Pattern Recognition · European Conference on Computer Vision · 2015–2024

A researcher in the Science corpus: 15 papers; trusts 1,244 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 565 citations into it; #13,563 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 336 citations into it; #27,379 of 35,345 by trust here, #10,798 by citations
• Automotive Engineering — 142 citations into it; #1,116 of 3,081 by trust here, #1,245 by citations
• Model Architectures — 65 citations into it
• Reinforcement Learning — 59 citations into it
• Evaluation & Benchmarks — 48 citations into it
• Control and Systems Engineering — 46 citations into it
• Robotics & Control — 37 citations into it

Whom Hongyang Li 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. Shaoqing Ren
6. Christian Szegedy
7. Yann LeCun
8. Yoshua Bengio
9. Diederik P. Kingma
10. Sergey Ioffe

Who trusts Hongyang Li overall

1. Han-Jia Ye
2. Qianru Sun
3. De-chuan Zhan
4. Su Lu
5. Ekaterina Iakovleva
6. Wanli Ouyang
7. Hexiang Hu
8. Yao-Hung Hubert Tsai
9. Xiahan Shi
10. Pranav Singh Chib

Papers from Hongyang Li's perspective

Ordered by how strongly the researchers Hongyang Li 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

• Finding Task-Relevant Features for Few-Shot Learning by Category Traversal (2019) — 381 citations
• Neural Network Encapsulation (2018) — 54 citations
• SOGNet: Scene Overlap Graph Network for Panoptic Segmentation (2019) — 64 citations
• Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection (2017) — 82 citations
• Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline (2022) — 395 citations
• Learning Deep Representation with Large-Scale Attributes (2015) — 23 citations
• NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking (2024) — 348 citations
• Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability (2024) — 342 citations
• End-to-End Autonomous Driving: Challenges and Frontiers (2023) — 874 citations
• DriveLM: Driving with Graph Visual Question Answering (2023) — 601 citations
• ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning (2022) — 499 citations
• BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision (2022) — 460 citations
• BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (2022) — 1,985 citations
• Planning-oriented Autonomous Driving (2022) — 1,470 citations
• DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks (2017) — 134 citations