Michael Gygli
Computer Vision and Pattern Recognition · 2013–2019
A researcher in the Science corpus: 7 papers; trusts 1,186 researchers and is trusted by 145 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 150 citations into it; #2,948 of 21,575 by trust here, #6,873 by citations
- Artificial Intelligence — 56 citations into it; #17,893 of 35,345 by trust here, #25,809 by citations
- Evaluation & Benchmarks — 29 citations into it
- Model Architectures — 15 citations into it
- Natural Language Processing — 14 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Signal Processing — 13 citations into it
- Generative Models — 8 citations into it; #1,038 of 1,956 by trust here, #1,796 by citations
Whom Michael Gygli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Ross B. Girshick
- Ilya Sutskever
- Jia Deng
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Karen Simonyan
Who trusts Michael Gygli overall
- Zhong Ji
- Kaiyang Zhou
- Medhini Narasimhan
- Bryan A. Plummer
- Jinsoo Choi
- Ana Garcia Del Molino
- Ke Zhang
- Chandrashekhar Lavania
- Evlampios Apostolidis
- Yu-Chuan Su
Papers from Michael Gygli's perspective
Ordered by how strongly the researchers Michael Gygli 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Going deeper with convolutions
Their papers
- Video summarization by learning submodular mixtures of objectives (2015) — 428 citations
- Creating Summaries from User Videos (2014) — 826 citations
- The Interestingness of Images (2013) — 203 citations
- Interactive Video Object Segmentation in the Wild (2017) — 52 citations
- Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs (2017) — 68 citations
- Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections (2019) — 64 citations
- Efficient Object Annotation via Speaking and Pointing (2019) — 18 citations