Massimo Minervini

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 9 papers; trusts 381 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 147 citations into it; #4,700 of 21,575 by trust here, #5,941 by citations
• Plant Science — 97 citations into it; #467 of 8,232 by trust here, #2,363 by citations
• Computational Biology & Bioinformatics — 59 citations into it
• Ecology — 23 citations into it; #1,211 of 10,717 by trust here, #4,784 by citations
• Biophysics — 17 citations into it; #1,082 of 1,196 by trust here, #880 by citations
• Molecular Biology — 17 citations into it; #8,799 of 17,773 by trust here, #13,284 by citations
• Artificial Intelligence — 16 citations into it
• Media Technology — 14 citations into it; #2,809 of 3,621 by trust here, #3,522 by citations

Whom Massimo Minervini trusts overall

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

1. Sotirios A. Tsaftaris
2. Hanno Scharr
3. Jean-Michel Pape
4. Victor S. Lempitsky
5. David G. Lowe
6. Andrew Zisserman
7. David R. Martin
8. Leo J. Grady
9. Anton Hartmann
10. Mario Valerio Giuffrida

Who trusts Massimo Minervini overall

1. Hanno Scharr
2. Sotirios A. Tsaftaris
3. Mario Valerio Giuffrida
4. Jean-Michel Pape
5. Jordan R. Ubbens
6. Andreas Fischbach
7. Christian Klukas
8. Mengye Ren
9. Ian Stavness
10. Lei Li

Papers from Massimo Minervini's perspective

Ordered by how strongly the researchers Massimo Minervini 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Finely-grained annotated datasets for image-based plant phenotyping (2016) — 306 citations
• Leaf segmentation in plant phenotyping: a collation study (2015) — 281 citations
• Annotated Image Datasets of Rosette Plants (2014) — 101 citations
• Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] (2015) — 242 citations
• Learning computationally efficient approximations of complex image segmentation metrics (2013) — 6 citations
• segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017)
• An interactive tool for semi-automated leaf annotation (2015) — 20 citations
• Learning to Count Leaves in Rosette Plants (2015) — 110 citations
• Image-based plant phenotyping with incremental learning and active contours (2014) — 135 citations