Markus Rempfler

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Markus Rempfler trusts

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

  1. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  2. Trevor Darrell (in Computer Vision and Pattern Recognition)

Papers from Markus Rempfler's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  3. Fully convolutional networks for semantic segmentation
  4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  5. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  6. Caffe: Convolutional Architecture for Fast Feature Embedding
  7. Fast R-CNN
  8. Squeeze-and-Excitation Networks
  9. YOLO9000: Better, Faster, Stronger
  10. Focal Loss for Dense Object Detection

Their papers