Gian Luca Foresti
Computer Vision and Pattern Recognition · 2008–2021
A researcher in the Science corpus: 4 papers; trusts 129 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 99 citations into it; #15,496 of 21,575 by trust here, #9,079 by citations
- Artificial Intelligence — 35 citations into it
- Biomedical Engineering — 14 citations into it
- Signal Processing — 6 citations into it
- Statistical Learning Theory — 5 citations into it
Whom Gian Luca Foresti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Xiaogang Wang
- John Shawe-Taylor
- Geoffrey E. Hinton
- Alex Krizhevsky
- Wei Li
- Bernhard Schölkopf
- Kaiming He
- Martin Hirzer
- Rui Zhao
- Yoshua Bengio
Who trusts Gian Luca Foresti overall
Papers from Gian Luca Foresti's perspective
Ordered by how strongly the researchers Gian Luca Foresti trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Saliency Weighted Features for Person Re-identification (2014) — 144 citations
- The Eighth Visual Object Tracking VOT2020 Challenge Results (2020) — 345 citations
- VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization (2021) — 523 citations
- Trajectory-Based Anomalous Event Detection (2008) — 536 citations