Christine M. Guillemot
Computer Vision and Pattern Recognition · 2012–2018
A researcher in the Science corpus: 4 papers; trusts 296 researchers and is trusted by 113 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 73 citations into it; #3,037 of 21,575 by trust here, #4,623 by citations
- Computational Mechanics — 14 citations into it
- Artificial Intelligence — 13 citations into it
- Computer Graphics and Computer-Aided Design — 12 citations into it
- Signal Processing — 12 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Generative Models — 6 citations into it
- Media Technology — 5 citations into it; #278 of 3,621 by trust here, #339 by citations
Whom Christine M. Guillemot trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexei A. Efros
- Kaiming He
- Marcelo Bertalmío
- Nikos Komodakis
- Michael Ashikhmin
- David Tschumperlé
- Alexandru Cristian Telea
- Geoffrey E. Hinton
- Vladimir Kolmogorov
- Shai Avidan
Who trusts Christine M. Guillemot overall
- Chao Dong
- Yulun Zhang
- Radu Timofte
- Saeed Anwar
- Kai Zhang
- Yong Guo
- Namhyuk Ahn
- Zheng Hui
- Ying Tai
- Jingyun Liang
Papers from Christine M. Guillemot's perspective
Ordered by how strongly the researchers Christine M. Guillemot trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding (2012) — 2,911 citations
- Autoencoder Based Image Compression: Can the Learning be Quantization Independent? (2018) — 59 citations
- Image compression with Stochastic Winner-Take-All Auto-Encoder (2017) — 27 citations
- Image Inpainting : Overview and Recent Advances (2014) — 604 citations