Sudhanshu Mittal

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 571 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 94 citations into it; #11,228 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 83 citations into it; #17,934 of 35,345 by trust here, #16,166 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,882 of 3,145 by trust here, #2,299 by citations
• Optimization & Training — 19 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Meta & Continual Learning — 17 citations into it; #635 of 1,041 by trust here, #897 by citations
• Model Architectures — 10 citations into it
• Natural Language Processing — 7 citations into it

Whom Sudhanshu Mittal trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Jia Deng
9. Shaoqing Ren
10. Ilya Sutskever

Who trusts Sudhanshu Mittal overall

1. Lihe Yang
2. Yabin Wang
3. Chong Zhou
4. Anna Kukleva
5. Yanbei Chen
6. Xiaokang Chen
7. Shikun Liu
8. Jieren Deng
9. Zhanghan Ke
10. Zhiwu Huang

Papers from Sudhanshu Mittal's perspective

Ordered by how strongly the researchers Sudhanshu Mittal 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. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency (2019) — 469 citations
• Open-vocabulary Attribute Detection (2022) — 42 citations
• Essentials for Class Incremental Learning (2021) — 119 citations
• Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset (2017) — 350 citations