Baekgyu Kim

Computer Vision and Pattern Recognition · 2012–2019

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

Subfields

Whom Baekgyu Kim trusts overall

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

  1. Joseph Redmon
  2. Mark Everingham
  3. Tsung-Yi Lin

Papers from Baekgyu Kim's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Microsoft COCO: Common Objects in Context
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  5. Gradient-based learning applied to document recognition
  6. ImageNet: A large-scale hierarchical image database
  7. ImageNet Large Scale Visual Recognition Challenge
  8. Going deeper with convolutions
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge

Their papers