Vighnesh Birodkar

Computer Vision and Pattern Recognition · 2017–2025

A researcher in the Science corpus: 5 papers; trusts 370 researchers and is trusted by 271 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 171 citations into it; #2,397 of 21,575 by trust here, #4,030 by citations
• Artificial Intelligence — 105 citations into it; #3,959 of 35,345 by trust here, #5,390 by citations
• Generative Models — 33 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #579 of 3,145 by trust here, #1,012 by citations
• Media Technology — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Vighnesh Birodkar trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Kaiming He
4. Alex Krizhevsky
5. Karen Simonyan
6. Diederik P. Kingma
7. Yoshua Bengio
8. Tim Salimans
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Vighnesh Birodkar overall

1. Aviv Gabbay
2. Francesco Locatello
3. Hsin-Ying Lee
4. David R Ha
5. Guo-Jun Qi
6. Yingzhen Li
7. Yedid Hoshen
8. Bo Zhao
9. Yotam Nitzan
10. Abdul Jabbar

Papers from Vighnesh Birodkar's perspective

Ordered by how strongly the researchers Vighnesh Birodkar 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Learning of Disentangled Representations from Video (2017) — 583 citations
• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• The iWildCam 2021 Competition Dataset (2021) — 105 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• VideoPoet: A Large Language Model for Zero-Shot Video Generation (2023) — 521 citations