Google Switzerland

Computer Vision and Pattern Recognition

A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Google Switzerland trusts overall

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

  1. David G. Lowe
  2. Josef Sivic
  3. Krystian Mikolajczyk

Papers from Google Switzerland's perspective

Ordered by how strongly the researchers Google Switzerland trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Distinctive Image Features from Scale-Invariant Keypoints
  2. Video Google: a text retrieval approach to object matching in videos
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Scalable Recognition with a Vocabulary Tree
  5. ImageNet classification with deep convolutional neural networks
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. ImageNet Large Scale Visual Recognition Challenge
  9. Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
  10. Going deeper with convolutions

Their papers