Didier Mutter

Computer Vision and Pattern Recognition · 2001–2016

A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Didier Mutter trusts overall

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

  1. Ross B. Girshick
  2. Alex Krizhevsky

Papers from Didier Mutter's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. ImageNet Large Scale Visual Recognition Challenge
  3. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  4. Object Detection with Discriminatively Trained Part Based Models
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
  7. Caffe: Convolutional Architecture for Fast Feature Embedding
  8. Gradient-based learning applied to document recognition
  9. Long Short-Term Memory
  10. ImageNet: A large-scale hierarchical image database

Their papers