Matej Kristan

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 343 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 416 citations into it; #8,557 of 21,575 by trust here, #1,992 by citations
• Artificial Intelligence — 81 citations into it; #16,091 of 35,345 by trust here, #13,962 by citations
• Evaluation & Benchmarks — 53 citations into it
• Electrical and Electronic Engineering — 34 citations into it
• Media Technology — 26 citations into it
• Biophysics — 9 citations into it
• Computer Networks and Communications — 6 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it

Whom Matej Kristan trusts overall

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

1. Martin Danelljan
2. Kaiming He
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Ross B. Girshick
7. Andrew Zisserman
8. Yi Wu
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Matej Kristan overall

1. Martin Danelljan
2. Goutam Bhat
3. Qiang Wang
4. Hui Li
5. Luca Bertinetto
6. Zhipeng Zhang
7. Bo Li
8. Matthias Müller
9. Ning Wang
10. Seyed Mojtaba Marvasti-Zadeh

Papers from Matej Kristan's perspective

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

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

Their papers

• The Visual Object Tracking VOT2015 Challenge Results (2015) — 901 citations
• The Visual Object Tracking VOT2014 Challenge Results (2014) — 512 citations
• The Visual Object Tracking VOT2016 Challenge Results (2016) — 773 citations
• A Novel Performance Evaluation Methodology for Single-Target Trackers (2015) — 661 citations
• Discriminative Correlation Filter Tracker with Channel and Spatial Reliability (2016) — 835 citations
• The Visual Object Tracking VOT2017 Challenge Results (2017) — 301 citations
• The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
• DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection (2021) — 1,151 citations
• The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
• The Seventh Visual Object Tracking VOT2019 Challenge Results (2019) — 486 citations
• Reconstruction by inpainting for visual anomaly detection (2020) — 732 citations