Piotr Koniusz

Computer Vision and Pattern Recognition · with Zhang · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 920 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #5,149 of 21,575 by trust here, #4,807 by citations
• Artificial Intelligence — 81 citations into it; #7,630 of 35,345 by trust here, #7,797 by citations
• Representation & Self-Supervised Learning — 40 citations into it; #2,626 of 3,145 by trust here, #2,691 by citations
• Media Technology — 19 citations into it; #2,194 of 3,621 by trust here, #1,135 by citations
• Biomedical Engineering — 15 citations into it
• Model Architectures — 13 citations into it; #1,133 of 2,278 by trust here, #1,131 by citations
• Evaluation & Benchmarks — 11 citations into it
• Human-Computer Interaction — 11 citations into it

Whom Piotr Koniusz trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Andrew Zisserman
5. Kaiming He
6. Yann LeCun
7. Yoshua Bengio
8. Ilya Sutskever
9. Ross B. Girshick
10. Diederik P. Kingma

Who trusts Piotr Koniusz overall

1. Saeid Motiian
2. Julien Mairal
3. Lei Wang
4. Zhiyuan Shi
5. Alberto Bietti
6. Jue Wang
7. Junwu Weng
8. Artem Rozantsev
9. Dapeng Hu
10. Ruijia Xu

Papers from Piotr Koniusz's perspective

Ordered by how strongly the researchers Piotr Koniusz trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Convolutional Kernel Networks (2014) — 402 citations
• Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring (2019) — 649 citations
• Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors (2016) — 125 citations
• Few-Shot Learning via Saliency-Guided Hallucination of Samples (2019) — 216 citations
• Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition With CNNs (2019) — 101 citations
• Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (2016) — 118 citations