Lorenzo Baraldi

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 10 papers; trusts 1,752 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 535 citations into it; #5,114 of 21,575 by trust here, #5,754 by citations
• Artificial Intelligence — 328 citations into it; #12,047 of 35,345 by trust here, #13,544 by citations
• Multimodal Learning — 264 citations into it; #302 of 1,211 by trust here, #610 by citations
• Natural Language Processing — 162 citations into it
• Evaluation & Benchmarks — 82 citations into it
• Model Architectures — 62 citations into it
• Natural Language Generation — 41 citations into it; #46 of 199 by trust here, #67 by citations
• Representation & Self-Supervised Learning — 32 citations into it

Whom Lorenzo Baraldi trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Karen Simonyan
8. Ross B. Girshick
9. Yann LeCun
10. Jimmy Ba

Who trusts Lorenzo Baraldi overall

1. Marcella Cornia
2. Matteo Stefanini
3. Rita Cucchiara
4. Giuseppe Fiameni
5. Tushar Nagarajan
6. Xuying Zhang
7. Shan Li
8. Aditya Mogadala
9. Edwin G. Ng
10. Jordi Pont-Tuset

Papers from Lorenzo Baraldi's perspective

Ordered by how strongly the researchers Lorenzo Baraldi 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Meshed-Memory Transformer for Image Captioning (2019) — 1,111 citations
• Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions (2018) — 199 citations
• Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model (2016) — 608 citations
• A deep multi-level network for saliency prediction (2016) — 375 citations
• From Show to Tell: A Survey on Deep Learning-Based Image Captioning (2021) — 395 citations
• Universal Captioner: Long-Tail Vision-and-Language Model Training through Content-Style Separation (2021) — 16 citations
• From Show to Tell: A Survey on Image Captioning (2021) — 58 citations
• Embodied Vision-and-Language Navigation with Dynamic Convolutional Filters (2019) — 28 citations
• SMArT: Training Shallow Memory-aware Transformers for Robotic Explainability (2019) — 29 citations
• Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention (2017) — 100 citations