Mengchen Liu
Computer Vision and Pattern Recognition · with Chen · 2019–2022
A researcher in the Science corpus: 10 papers; trusts 966 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 429 citations into it; #4,560 of 21,575 by trust here, #1,904 by citations
- Artificial Intelligence — 317 citations into it; #26,092 of 35,345 by trust here, #5,359 by citations
- Model Architectures — 148 citations into it; #805 of 2,278 by trust here, #329 by citations
- Efficiency & Compression — 97 citations into it
- Representation & Self-Supervised Learning — 57 citations into it
- Optimization & Training — 42 citations into it
- Evaluation & Benchmarks — 38 citations into it
- Large Language Models — 21 citations into it
Whom Mengchen Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Diederik P. Kingma
- Yoshua Bengio
- Ross B. Girshick
- Ilya Sutskever
- Jia Deng
- Karen Simonyan
Who trusts Mengchen Liu overall
- Jianwei Yang
- Sachin Mehta
- Pavan Kumar Anasosalu Vasu
- Yinpeng Chen
- Xiyang Dai
- Chien-Yao Wang
- Lu Yuan
- Chao Li
- Jianfeng Gao
- Pengchuan Zhang
Papers from Mengchen Liu's perspective
Ordered by how strongly the researchers Mengchen 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Florence: A New Foundation Model for Computer Vision (2021) — 1,142 citations
- CvT: Introducing Convolutions to Vision Transformers (2021) — 2,478 citations
- Dynamic Head: Unifying Object Detection Heads with Attentions (2021) — 964 citations
- Dynamic Convolution: Attention Over Convolution Kernels (2019) — 1,399 citations
- BEVT: BERT Pretraining of Video Transformers (2021) — 269 citations
- Mobile-Former: Bridging MobileNet and Transformer (2021) — 695 citations
- Dynamic ReLU (2020) — 221 citations
- TinyViT: Fast Pretraining Distillation for Small Vision Transformers (2022) — 538 citations
- Residual Mixture of Experts (2022) — 55 citations
- Revisiting Dynamic Convolution via Matrix Decomposition (2021) — 86 citations