Aishwarya Agrawal

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 829 researchers and is trusted by 2,134 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 231 citations into it; #858 of 21,575 by trust here, #580 by citations
• Artificial Intelligence — 179 citations into it; #900 of 35,345 by trust here, #723 by citations
• Multimodal Learning — 111 citations into it; #86 of 1,211 by trust here, #49 by citations
• Natural Language Processing — 77 citations into it; #1,639 of 5,688 by trust here, #1,174 by citations
• Evaluation & Benchmarks — 26 citations into it
• Large Language Models — 17 citations into it
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Aishwarya Agrawal trusts overall

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

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

Who trusts Aishwarya Agrawal overall

1. Kushal Kafle
2. Zhe Gan
3. Damien Teney
4. Qi Wu
5. Mohit Bansal
6. Devi Parikh
7. Linjie Li
8. Drew A. Hudson
9. Dhruv Batra
10. Jiasen Lu

Papers from Aishwarya Agrawal's perspective

Ordered by how strongly the researchers Aishwarya Agrawal trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• VQA: Visual Question Answering (2015) — 6,567 citations
• Visual Storytelling (2016) — 543 citations
• Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering (2017) — 723 citations
• Analyzing the Behavior of Visual Question Answering Models (2016) — 344 citations
• Measuring Machine Intelligence Through Visual Question Answering (2016) — 38 citations
• Overcoming Language Priors in Visual Question Answering with Adversarial Regularization (2018) — 272 citations
• Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes (2016) — 31 citations
• MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting (2022) — 59 citations