Francisco Massa
Computer Vision and Pattern Recognition · 2015–2023
A researcher in the Science corpus: 8 papers; trusts 866 researchers and is trusted by 2,559 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 283 citations into it; #760 of 21,575 by trust here, #503 by citations
- Artificial Intelligence — 209 citations into it; #1,645 of 35,345 by trust here, #300 by citations
- Model Architectures — 56 citations into it; #296 of 2,278 by trust here, #162 by citations
- Evaluation & Benchmarks — 51 citations into it
- Representation & Self-Supervised Learning — 50 citations into it
- Natural Language Processing — 26 citations into it
- Optimization & Training — 26 citations into it
- Efficiency & Compression — 24 citations into it; #151 of 1,264 by trust here, #70 by citations
Whom Francisco Massa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- David G. Lowe
- Ilya Sutskever
- Yann LeCun
- Diederik P. Kingma
- Karen Simonyan
Who trusts Francisco Massa overall
- Jianwei Yang
- Ronghang Hu
- Weihao Yu
- Ishan Misra
- Kai Han
- Da-Wei Zhou
- Hugo Touvron
- Gabriella Csurka
- Chunyuan Li
- Zhe Gan
Papers from Francisco Massa's perspective
Ordered by how strongly the researchers Francisco Massa 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- End-to-End Object Detection with Transformers (2020) — 19,102 citations
- Training data-efficient image transformers & distillation through attention (2020) — 9,450 citations
- PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019) — 53,912 citations
- DINOv2: Learning Robust Visual Features without Supervision (2023) — 9,372 citations
- Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views (2015) — 100 citations
- MLPerf Inference Benchmark (2019) — 720 citations
- Hybrid Transformers for Music Source Separation (2022) — 298 citations
- The Vision Behind MLPerf: Understanding AI Inference Performance (2021) — 19 citations