Qiang Li
Computer Vision and Pattern Recognition · with Wang · 2007–2022
A researcher in the Science corpus: 5 papers; trusts 207 researchers and is trusted by 54 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 102 citations into it; #3,454 of 21,575 by trust here, #7,499 by citations
- Artificial Intelligence — 39 citations into it
- Generative Models — 27 citations into it
- Media Technology — 20 citations into it; #266 of 3,621 by trust here, #1,002 by citations
- Cognitive Neuroscience — 12 citations into it
- Representation & Self-Supervised Learning — 12 citations into it
- Evaluation & Benchmarks — 8 citations into it
Whom Qiang Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eero P. Simoncelli
- Zhou Wang
- Hamid R. Sheikh
- A. Bovik
- Martin J. Wainwright
- Damon M. Chandler
- Javier Portilla
- Kaiming He
- Geoffrey E. Hinton
- Odelia Schwartz
Who trusts Qiang Li overall
- A. Bovik
- Zhou Wang
- Damon M. Chandler
- Rajiv Soundararajan
- Kede Ma
- Yanjiao Chen
- Anush K. Moorthy
- Yochai Blau
- Michele A. Saad
- Peng Ye
Papers from Qiang Li's perspective
Ordered by how strongly the researchers Qiang Li 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
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- CRIS: CLIP-Driven Referring Image Segmentation (2021) — 540 citations
- Information Content Weighting for Perceptual Image Quality Assessment (2011) — 1,316 citations
- Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation (2009) — 358 citations
- Video Quality Assessment Using a Statistical Model of Human Visual Speed Perception (2007)
- Bridging CLIP and StyleGAN through Latent Alignment for Image Editing (2022) — 16 citations