Yifan Liu
Computer Vision and Pattern Recognition · 2017–2023
A researcher in the Science corpus: 6 papers; trusts 880 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 221 citations into it; #10,297 of 21,575 by trust here, #6,146 by citations
- Artificial Intelligence — 120 citations into it; #16,746 of 35,345 by trust here, #11,327 by citations
- Model Architectures — 30 citations into it
- Generative Models — 22 citations into it
- Efficiency & Compression — 19 citations into it; #1,238 of 1,264 by trust here, #857 by citations
- Evaluation & Benchmarks — 16 citations into it
- Aerospace Engineering — 13 citations into it
- Prompting & Reasoning — 9 citations into it
Whom Yifan Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Ilya Sutskever
- Karen Simonyan
- Shaoqing Ren
Who trusts Yifan Liu overall
- Ekin Dogus Cubuk
- Wei Yin
- Sangyong Park
- Juan R. Terven
- Barret Zoph
- Chunhua Shen
- Linfeng Zhang
- Yuang Liu
- Xinlong Wang
- Jianping Gou
Papers from Yifan Liu's perspective
Ordered by how strongly the researchers Yifan Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Structured Knowledge Distillation for Semantic Segmentation (2019) — 708 citations
- Data Augmentation in Emotion Classification Using Generative Adversarial Networks (2017) — 121 citations
- Segment Anything in High Quality (2023) — 655 citations
- Enforcing Geometric Constraints of Virtual Normal for Depth Prediction (2019) — 494 citations
- ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation (2022) — 313 citations
- Channel-wise Knowledge Distillation for Dense Prediction* (2020) — 462 citations