Songwei Ge
Computer Vision and Pattern Recognition · 2019–2023
A researcher in the Science corpus: 4 papers; trusts 421 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 156 citations into it; #9,193 of 21,575 by trust here, #4,525 by citations
- Artificial Intelligence — 76 citations into it; #18,554 of 35,345 by trust here, #8,810 by citations
- Generative Models — 74 citations into it
- Representation & Self-Supervised Learning — 43 citations into it; #2,004 of 3,145 by trust here, #1,159 by citations
- Computer Graphics and Computer-Aided Design — 15 citations into it
- Evaluation & Benchmarks — 12 citations into it
- Optimization & Training — 9 citations into it
- Control and Systems Engineering — 6 citations into it
Whom Songwei Ge trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Karen Simonyan
- Tero Karras
- Jimmy Ba
Who trusts Songwei Ge overall
- Lijun Yu
- Agrim Gupta
- Muhammad Uzair Khattak
- Hieu Pham
- Andreas Blattmann
- Haoxin Chen
- Yin-Yin He
- Wenyi Hong
- Michael Matena
- Vinay Uday Prabhu
Papers from Songwei Ge's perspective
Ordered by how strongly the researchers Songwei Ge 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)
- ImageNet Large Scale Visual Recognition Challenge
- GENERATIVE ADVERSARIAL NETS
Their papers
- Learning Robust Global Representations by Penalizing Local Predictive Power (2019) — 1,357 citations
- Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer (2022) — 309 citations
- Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models (2023) — 326 citations
- Creative Sketch Generation (2020) — 93 citations