Amit Kumar Singh

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it

Whom Amit Kumar Singh trusts overall

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

1. Liang-Chieh Chen
2. Charlotte Pelletier
3. John E. Ball
4. Ross B. Girshick
5. Kaiming He

Papers from Amit Kumar Singh's perspective

Ordered by how strongly the researchers Amit Kumar Singh 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Going deeper with convolutions
6. ImageNet Large Scale Visual Recognition Challenge
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Review on remote sensing methods for landslide detection using machine and deep learning (2020) — 295 citations
• An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE (2019) — 348 citations