Medhini Narasimhan

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 889 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 120 citations into it; #8,413 of 21,575 by trust here, #11,950 by citations
• Artificial Intelligence — 107 citations into it; #14,221 of 35,345 by trust here, #20,836 by citations
• Multimodal Learning — 53 citations into it; #446 of 1,211 by trust here, #962 by citations
• Natural Language Processing — 41 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Aerospace Engineering — 16 citations into it
• Reinforcement Learning — 16 citations into it
• Model Architectures — 14 citations into it

Whom Medhini Narasimhan trusts overall

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

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

Who trusts Medhini Narasimhan overall

1. Guohao Li
2. Jialin Wu
3. François Gardères
4. P. Xu
5. Zihao Zhu
6. Kenneth Marino
7. Xin Wang
8. Jiasen Lu
9. Maryam Ziaeefard
10. Xiatian Zhu

Papers from Medhini Narasimhan's perspective

Ordered by how strongly the researchers Medhini Narasimhan 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. 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

• Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering (2018) — 119 citations
• Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation (2020) — 66 citations
• Modular Visual Question Answering via Code Generation (2023) — 68 citations
• CLIP-It! Language-Guided Video Summarization (2021) — 180 citations