Vittal Premachandran

Computer Vision and Pattern Recognition · 2014–2025

A researcher in the Science corpus: 6 papers; trusts 821 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 94 citations into it; #8,140 of 21,575 by trust here, #12,172 by citations
• Artificial Intelligence — 40 citations into it; #14,260 of 35,345 by trust here, #21,316 by citations
• Evaluation & Benchmarks — 14 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Media Technology — 9 citations into it
• Model Architectures — 9 citations into it
• Representation & Self-Supervised Learning — 7 citations into it; #2,452 of 3,145 by trust here, #2,957 by citations
• Aerospace Engineering — 6 citations into it

Whom Vittal Premachandran trusts overall

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

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ross B. Girshick
6. Piotr Doll'ar
7. Andrew Zisserman
8. Kaiming He
9. Yann LeCun
10. Ilya Sutskever

Who trusts Vittal Premachandran overall

1. Md.Atiqur Rahman
2. Diane Bouchacourt
3. Alan L. Yuille
4. Yang Wang
5. Zhishuai Zhang
6. Zhuotun Zhu
7. Pawan Kumar Mudigonda
8. Cihang Xie
9. Lingxi Xie
10. Adam Kortylewski

Papers from Vittal Premachandran's perspective

Ordered by how strongly the researchers Vittal Premachandran 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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

• Discovering Internal Representations from Object-CNNs Using Population Encoding (2015) — 17 citations
• Visual Concepts and Compositional Voting (2017) — 35 citations
• Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters (2014) — 26 citations
• Unsupervised learning of object semantic parts from internal states of CNNs by population encoding (2015) — 44 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset (2015) — 3 citations