Marcella Cornia

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 9 papers; trusts 1,713 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 517 citations into it; #5,131 of 21,575 by trust here, #5,843 by citations
• Artificial Intelligence — 314 citations into it; #12,210 of 35,345 by trust here, #13,962 by citations
• Multimodal Learning — 254 citations into it; #300 of 1,211 by trust here, #610 by citations
• Natural Language Processing — 152 citations into it
• Evaluation & Benchmarks — 80 citations into it
• Model Architectures — 60 citations into it
• Natural Language Generation — 41 citations into it; #47 of 199 by trust here, #67 by citations
• Representation & Self-Supervised Learning — 30 citations into it

Whom Marcella Cornia trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ilya Sutskever
7. Ross B. Girshick
8. Karen Simonyan
9. Oriol Vinyals
10. Shaoqing Ren

Who trusts Marcella Cornia overall

1. Lorenzo Baraldi
2. Matteo Stefanini
3. Rita Cucchiara
4. Giuseppe Fiameni
5. Tushar Nagarajan
6. Xuying Zhang
7. Yuhao Zhang
8. Gianni Brauwers
9. Viktar Atliha
10. Shan Li

Papers from Marcella Cornia's perspective

Ordered by how strongly the researchers Marcella Cornia 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
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
• 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