Loris Bazzani

Computer Vision and Pattern Recognition · 2010–2016

A researcher in the Science corpus: 8 papers; trusts 827 researchers and is trusted by 541 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 165 citations into it; #1,832 of 21,575 by trust here, #2,347 by citations
• Artificial Intelligence — 56 citations into it; #16,807 of 35,345 by trust here, #10,667 by citations
• Aerospace Engineering — 12 citations into it
• Media Technology — 12 citations into it
• Cognitive Neuroscience — 11 citations into it; #6,531 of 10,852 by trust here, #4,170 by citations
• Probabilistic & Bayesian Methods — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Loris Bazzani trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ross B. Girshick
4. Karen Simonyan
5. Kaiming He
6. Alex Graves
7. Yoshua Bengio
8. Christian Szegedy
9. Yann LeCun
10. Andrew Zisserman

Who trusts Loris Bazzani overall

1. Liang Zheng
2. Rui Zhao
3. Shengcai Liao
4. Xiaogang Wang
5. Yi Yang
6. Wanli Ouyang
7. Riccardo Satta
8. Xinlei Chen
9. Shaogang Gong
10. Xiatian Zhu

Papers from Loris Bazzani's perspective

Ordered by how strongly the researchers Loris Bazzani 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Learning Where to Attend with Deep Architectures for Image Tracking (2011) — 210 citations
• Person re-identification by symmetry-driven accumulation of local features (2010) — 1,654 citations
• Self-taught object localization with deep networks (2014) — 160 citations
• Custom Pictorial Structures for Re-identification (2011) — 722 citations
• Re-identification with RGB-D Sensors (2012) — 215 citations
• Recurrent Mixture Density Network for Spatiotemporal Visual Attention (2016) — 142 citations
• Symmetry-driven accumulation of local features for human characterization and re-identification (2013) — 326 citations
• Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification (2016) — 23 citations