Salvatore Candido

Computer Vision and Pattern Recognition · bioRxiv · 2021–2025

A researcher in the Science corpus: 5 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Salvatore Candido trusts

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

  1. Kaiming He (in Computer Vision and Pattern Recognition)
  2. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  3. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  4. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  5. Trevor Darrell (in Computer Vision and Pattern Recognition)
  6. Piotr Doll'ar (in Computer Vision and Pattern Recognition)
  7. Jitendra Malik (in Computer Vision and Pattern Recognition)
  8. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  9. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  10. Deva Ramanan (in Computer Vision and Pattern Recognition)

Papers from Salvatore Candido's perspective

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

  1. Attention is All you Need (Neural Information Processing Systems)
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. Visualizing and Understanding Convolutional Networks
  6. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
  7. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
  8. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
  9. A Simple Framework for Contrastive Learning of Visual Representations (International Conference on Machine Learning)
  10. ImageNet: A large-scale hierarchical image database

Their papers