Ramprasaath R. Selvaraju
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 8 papers; trusts 735 researchers and is trusted by 833 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 186 citations into it; #1,537 of 21,575 by trust here, #656 by citations
- Artificial Intelligence — 166 citations into it; #3,015 of 35,345 by trust here, #957 by citations
- Multimodal Learning — 78 citations into it; #855 of 1,211 by trust here, #390 by citations
- Natural Language Processing — 66 citations into it; #5,659 of 5,688 by trust here, #2,333 by citations
- Representation & Self-Supervised Learning — 34 citations into it; #2,693 of 3,145 by trust here, #939 by citations
- Evaluation & Benchmarks — 26 citations into it
- Model Architectures — 20 citations into it
- Safety, Robustness & Interpretability — 18 citations into it; #317 of 2,251 by trust here, #54 by citations
Whom Ramprasaath R. Selvaraju trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Ross B. Girshick
- Karen Simonyan
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Andrew Zisserman
Who trusts Ramprasaath R. Selvaraju overall
- Quanshi Zhang
- Laura Rieger
- Wojciech Samek
- Trevor Darrell
- Zizhao Zhang
- Song-Chun Zhu
- Zhe Gan
- Amit Dhurandhar
- Yaoyao Liu
- Bernt Schiele
Papers from Ramprasaath R. Selvaraju's perspective
Ordered by how strongly the researchers Ramprasaath R. Selvaraju 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
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization (2016) — 28,183 citations
- Align before Fuse: Vision and Language Representation Learning with Momentum Distillation (2021) — 2,847 citations
- Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models (2016) — 657 citations
- Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization (2016) — 1,698 citations
- Diverse Beam Search for Improved Description of Complex Scenes (2018) — 289 citations
- Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded (2019) — 299 citations
- Grad-CAM: Why did you say that? (2016) — 616 citations
- Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance (2018) — 38 citations