A. Piergiovanni

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 14 papers; trusts 1,627 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 510 citations into it; #4,142 of 21,575 by trust here, #3,489 by citations
• Artificial Intelligence — 414 citations into it; #24,218 of 35,345 by trust here, #5,059 by citations
• Multimodal Learning — 132 citations into it
• Model Architectures — 84 citations into it
• Evaluation & Benchmarks — 81 citations into it
• Representation & Self-Supervised Learning — 71 citations into it
• Natural Language Processing — 46 citations into it
• Reinforcement Learning — 30 citations into it; #1,887 of 2,242 by trust here, #2,160 by citations

Whom A. Piergiovanni trusts overall

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

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

Who trusts A. Piergiovanni overall

1. Christoph Feichtenhofer
2. Santhosh K. Ramakrishnan
3. Humam Alwassel
4. Jean-Baptiste Alayrac
5. Yisheng Song
6. Jie Gui
7. Zehua Sun
8. Yuqiao Liu
9. Michael S. Ryoo
10. Xi Chen

Papers from A. Piergiovanni's perspective

Ordered by how strongly the researchers A. Piergiovanni 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• Evolving Losses for Unsupervised Video Representation Learning (2020) — 149 citations
• TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? (2021) — 181 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Learning Real-World Robot Policies by Dreaming (2018) — 33 citations
• Evolving Space-Time Neural Architectures for Videos (2018) — 62 citations
• F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models (2022) — 199 citations
• Pre-training image-language transformers for open-vocabulary tasks (2022) — 13 citations
• AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures (2019) — 110 citations
• Evolving Losses for Unlabeled Video Representation Learning (2019) — 7 citations
• Representation Flow for Action Recognition (2018) — 165 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• FindIt: Generalized Localization with Natural Language Queries (2022) — 18 citations
• AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification (2020) — 54 citations