Songze Li

Computer Vision and Pattern Recognition · arXiv.org · 2024–2025

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Songze Li trusts

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

  1. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  2. Kaiming He (in Computer Vision and Pattern Recognition)
  3. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  4. Jitendra Malik (in Computer Vision and Pattern Recognition)
  5. Li Fei-Fei (in Computer Vision and Pattern Recognition)
  6. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  8. Trevor Darrell (in Computer Vision and Pattern Recognition)
  9. Karen Simonyan (in Computer Vision and Pattern Recognition)
  10. David G. Lowe (in Computer Vision and Pattern Recognition)

Papers from Songze Li's perspective

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

  1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  2. Microsoft COCO: Common Objects in Context
  3. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
  4. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
  5. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
  6. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
  7. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
  8. HMDB: A large video database for human motion recognition
  9. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
  10. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Their papers