Clément Mallet

Computer Vision and Pattern Recognition · 2009–2015

A researcher in the Science corpus: 6 papers; trusts 25 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 43 citations into it; #8,540 of 21,575 by trust here, #17,258 by citations
• Environmental Engineering — 30 citations into it; #583 of 5,479 by trust here, #931 by citations
• Geology — 26 citations into it; #108 of 687 by trust here, #160 by citations
• Aerospace Engineering — 20 citations into it
• Ecology — 8 citations into it; #5,935 of 10,717 by trust here, #8,179 by citations
• Media Technology — 5 citations into it

Whom Clément Mallet trusts overall

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

1. Arthur P. Dempster
2. Daniel Munoz
3. Yair Weiss
4. Peter J. Green
5. Yasutaka Furukawa
6. Nan M. Laird
7. David Belton
8. Brian Curless
9. Niloy J. Mitra
10. Mahesh Pal

Who trusts Clément Mallet overall

1. Li Guo
2. Timo Hackel
3. Alexandre Boulch
4. Francesco Pirotti
5. Joel Janai
6. Weixiao Gao
7. Jing Huang
8. Nesrine Chehata
9. Victor Francisco Rodriguez-Galiano
10. Fabio Remondino

Papers from Clément Mallet's perspective

Ordered by how strongly the researchers Clément Mallet 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Distinctive Image Features from Scale-Invariant Keypoints
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation (2012) — 371 citations
• Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers (2015) — 672 citations
• Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge (2013) — 205 citations
• DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012) — 353 citations
• Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011) — 457 citations
• Full-waveform topographic lidar : State-of-the-art (2009) — 863 citations