Seunghak Shin

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Seunghak Shin trusts overall

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

  1. Ross B. Girshick

Papers from Seunghak Shin's perspective

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

  1. ImageNet: A large-scale hierarchical image database
  2. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  3. Histograms of oriented gradients for human detection
  4. ImageNet classification with deep convolutional neural networks
  5. Microsoft COCO: Common Objects in Context
  6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  7. Fully convolutional networks for semantic segmentation
  8. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  9. Gradient-based learning applied to document recognition
  10. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Their papers