Adrià Recasens
Computer Vision and Pattern Recognition · 2018–2025
A researcher in the Science corpus: 10 papers; trusts 721 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 331 citations into it; #9,465 of 21,575 by trust here, #4,623 by citations
- Artificial Intelligence — 199 citations into it
- Representation & Self-Supervised Learning — 116 citations into it; #1,800 of 3,145 by trust here, #982 by citations
- Multimodal Learning — 84 citations into it; #596 of 1,211 by trust here, #353 by citations
- Signal Processing — 84 citations into it; #3,970 of 7,572 by trust here, #1,629 by citations
- Evaluation & Benchmarks — 51 citations into it
- Speech & Audio — 39 citations into it
- Model Architectures — 25 citations into it
Whom Adrià Recasens trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Andrew Zisserman
- Karen Simonyan
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
Who trusts Adrià Recasens overall
- David F. Harwath
- Wei-Ning Hsu
- James R. Glass
- Grzegorz Chrupała
- Heliang Zheng
- Shuai Yang
- Antoine Miech
- Jean-Baptiste Alayrac
- Andrew Rouditchenko
- Christoph Feichtenhofer
Papers from Adrià Recasens's perspective
Ordered by how strongly the researchers Adrià Recasens 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Self-Supervised MultiModal Versatile Networks (2020) — 416 citations
- Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input (2018) — 213 citations
- Gaze360: Physically Unconstrained Gaze Estimation in the Wild (2019) — 494 citations
- TAP-Vid: A Benchmark for Tracking Any Point in a Video (2022) — 317 citations
- Broaden Your Views for Self-Supervised Video Learning (2021) — 146 citations
- Multimodal Self-Supervised Learning of General Audio Representations (2021) — 46 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks (2018) — 153 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Learning Words by Drawing Images (2019) — 9 citations