Sajid Shah

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Sajid Shah trusts overall

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

  1. David G. Lowe
  2. François Chollet
  3. Timothy Dozat
  4. Andrew P. Bradley
  5. Kaiming He

Papers from Sajid Shah's perspective

Ordered by how strongly the researchers Sajid Shah 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. Histograms of oriented gradients for human detection
  3. Rethinking the Inception Architecture for Computer Vision
  4. ImageNet classification with deep convolutional neural networks
  5. Densely Connected Convolutional Networks
  6. Object recognition from local scale-invariant features
  7. Going deeper with convolutions
  8. Gradient-based learning applied to document recognition
  9. Xception: Deep Learning with Depthwise Separable Convolutions
  10. ImageNet: A large-scale hierarchical image database

Their papers