Anurag Mittal

Computer Vision and Pattern Recognition · 2003–2018

A researcher in the Science corpus: 7 papers; trusts 335 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #4,809 of 21,575 by trust here, #6,954 by citations
• Artificial Intelligence — 42 citations into it; #15,391 of 35,345 by trust here, #14,718 by citations
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #1,591 of 3,265 by trust here, #1,741 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Generative Models — 14 citations into it
• Meta & Continual Learning — 8 citations into it

Whom Anurag Mittal trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Nir Friedman
5. Ahmed M. Elgammal
6. Yoshua Bengio
7. Yann LeCun
8. Leo Breiman
9. Matt P. Wand
10. Jing Zhong

Who trusts Anurag Mittal overall

1. Saad M. Khan
2. Mubarak Shah
3. Thierry Bouwmans
4. Omar Javed
5. Zoran Zivkovic
6. Xiaowei Zhou
7. Jérôme Berclaz
8. Franccois Fleuret
9. Yaser Sheikh
10. Alper Yilmaz

Papers from Anurag Mittal's perspective

Ordered by how strongly the researchers Anurag Mittal 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Background modeling and subtraction of dynamic scenes (2003) — 482 citations
• A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders (2017) — 328 citations
• M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene (2003) — 437 citations
• A Zero-Shot Framework for Sketch-based Image Retrieval (2018) — 217 citations
• Automated feature extraction for early detection of diabetic retinopathy in fundus images (2009) — 324 citations
• Nano-CMOS Circuit and Physical Design (2004) — 159 citations
• Motion-based background subtraction using adaptive kernel density estimation (2004) — 738 citations