Aruni RoyChowdhury

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 5 papers; trusts 1,257 researchers and is trusted by 829 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 253 citations into it; #1,520 of 21,575 by trust here, #3,222 by citations
• Artificial Intelligence — 103 citations into it; #2,473 of 35,345 by trust here, #4,627 by citations
• Representation & Self-Supervised Learning — 33 citations into it; #342 of 3,145 by trust here, #657 by citations
• Signal Processing — 29 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Model Architectures — 16 citations into it
• Aerospace Engineering — 15 citations into it
• Media Technology — 15 citations into it

Whom Aruni RoyChowdhury trusts overall

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

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

Who trusts Aruni RoyChowdhury overall

1. Heliang Zheng
2. Jianlong Fu
3. Xiu-Shen Wei
4. Yin Cui
5. Tsung-Yu Lin
6. Zhou Yu
7. Yuxin Peng
8. Peiqin Zhuang
9. Dacheng Tao
10. Xiangteng He

Papers from Aruni RoyChowdhury's perspective

Ordered by how strongly the researchers Aruni RoyChowdhury 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Bilinear CNN Models for Fine-Grained Visual Recognition (2015) — 2,076 citations
• Unsupervised Hard Example Mining from Videos for Improved Object Detection (2018) — 69 citations
• Labeled Faces in the Wild: A Survey (2016) — 485 citations
• Automatic Adaptation of Object Detectors to New Domains Using Self-Training (2019) — 184 citations
• Bilinear CNNs for Fine-grained Visual Recognition (2015) — 97 citations