Chaofeng Chen
Computer Vision and Pattern Recognition · 2020–2023
A researcher in the Science corpus: 5 papers; trusts 291 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 154 citations into it
- Media Technology — 59 citations into it
- Artificial Intelligence — 31 citations into it
- Evaluation & Benchmarks — 30 citations into it
- Model Architectures — 16 citations into it
- Signal Processing — 7 citations into it
- Optimization & Training — 5 citations into it
Whom Chaofeng Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Bovik
- Kaiming He
- Zhou Wang
- Diederik P. Kingma
- Geoffrey E. Hinton
- Hamid R. Sheikh
- Alex Krizhevsky
- Karen Simonyan
- Anish Mittal
- Yoshua Bengio
Who trusts Chaofeng Chen overall
Papers from Chaofeng Chen's perspective
Ordered by how strongly the researchers Chaofeng Chen 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Blind Face Restoration via Deep Multi-scale Component Dictionaries (2020) — 228 citations
- TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment (2023) — 346 citations
- Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels (2023) — 571 citations
- FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling (2022) — 327 citations
- Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives (2022) — 427 citations