Niluthpol Chowdhury Mithun

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 574 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #15,442 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 69 citations into it; #27,301 of 35,345 by trust here, #13,962 by citations
• Multimodal Learning — 53 citations into it; #909 of 1,211 by trust here, #758 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Natural Language Processing — 24 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Niluthpol Chowdhury Mithun trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Karen Simonyan
7. Ilya Sutskever
8. Andrew Zisserman
9. Li Fei-Fei
10. Richard Socher

Who trusts Niluthpol Chowdhury Mithun overall

1. Antoine Miech
2. Jean-Baptiste Alayrac
3. Xiaohan Wang
4. Jes'us Andr'es Portillo-Quintero
5. Fei Zhao
6. Simon Ging
7. Elad Amrani
8. Michael Wray
9. Valentin Gabeur
10. Lisa Anne Hendricks

Papers from Niluthpol Chowdhury Mithun's perspective

Ordered by how strongly the researchers Niluthpol Chowdhury Mithun 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval (2018) — 286 citations
• Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval (2018) — 81 citations
• Generating Diverse Image Datasets with Limited Labeling (2016) — 12 citations