Pia Helen Smedsrud
Computer Vision and Pattern Recognition · 2019–2021
A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 54 citations into it; #12,579 of 21,575 by trust here, #9,079 by citations
- Oncology — 39 citations into it; #512 of 1,698 by trust here, #867 by citations
- Artificial Intelligence — 25 citations into it
- Model Architectures — 12 citations into it
- Media Technology — 7 citations into it
- Ocean Engineering — 5 citations into it
Whom Pia Helen Smedsrud trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jorge Bernal
- Kaiming He
- Zhengxin Zhang
- Debesh Jha
- Olaf Ronneberger
- X. Zhang
- François Chollet
- F. Sánchez
- Shaoqing Ren
- Liang-Chieh Chen
Who trusts Pia Helen Smedsrud overall
- Debesh Jha
- Md Mostafijur Rahman
- Xiaoqi Zhao
- Taehun Kim
- Yundong Zhang
- Tao Zhou
- B. Dong
- JunChao Wei
- Deng-Ping Fan
- Ange Lou
Papers from Pia Helen Smedsrud's perspective
Ordered by how strongly the researchers Pia Helen Smedsrud 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
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021) — 307 citations
- HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy (2019) — 566 citations
- Kvasir-SEG: A Segmented Polyp Dataset (2019) — 1,927 citations
- ResUNet++: An Advanced Architecture for Medical Image Segmentation (2019) — 1,345 citations