Debidatta Dwibedi

Computer Vision and Pattern Recognition · 2017–2025

A researcher in the Science corpus: 9 papers; trusts 1,002 researchers and is trusted by 210 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #2,670 of 21,575 by trust here, #5,422 by citations
• Artificial Intelligence — 183 citations into it; #16,007 of 35,345 by trust here, #10,566 by citations
• Reinforcement Learning — 92 citations into it; #2,078 of 2,242 by trust here, #1,241 by citations
• Representation & Self-Supervised Learning — 70 citations into it; #2,837 of 3,145 by trust here, #1,866 by citations
• Control and Systems Engineering — 45 citations into it
• Optimization & Training — 27 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Aerospace Engineering — 18 citations into it

Whom Debidatta Dwibedi trusts overall

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

1. Kaiming He
2. David G. Lowe
3. Geoffrey E. Hinton
4. Andrew Zisserman
5. Alex Krizhevsky
6. Karen Simonyan
7. Yoshua Bengio
8. Ross B. Girshick
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Debidatta Dwibedi overall

1. Robert Dadashi
2. Prateek Katiyar
3. João Carreira
4. Andrew Zisserman
5. Ekin Dogus Cubuk
6. Raphael Gontijo Lopes
7. A. Srinivas
8. Nikita Dvornik
9. Yue Wu
10. Yusuf Aytar

Papers from Debidatta Dwibedi's perspective

Ordered by how strongly the researchers Debidatta Dwibedi 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection (2017) — 709 citations
• Temporal Cycle-Consistency Learning (2019) — 324 citations
• Learning Actionable Representations from Visual Observations (2018) — 84 citations
• Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning (2018) — 296 citations
• With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations (2021) — 552 citations
• XIRL: Cross-embodiment Inverse Reinforcement Learning (2021) — 160 citations
• Counting Out Time: Class Agnostic Video Repetition Counting in the Wild (2020) — 140 citations
• Gemini Robotics: Bringing AI into the Physical World (2025) — 385 citations
• Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning (2018) — 111 citations