Xichen Pan

Computer Vision and Pattern Recognition · 2024–2025

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Xichen Pan 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. Saining Xie (in Computer Vision and Pattern Recognition)
  4. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  5. Piotr Doll'ar (in Computer Vision and Pattern Recognition)
  6. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  7. Li Fei-Fei (in Computer Vision and Pattern Recognition)
  8. Jitendra Malik (in Computer Vision and Pattern Recognition)
  9. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  10. Yann LeCun (in Computer Vision and Pattern Recognition)

Papers from Xichen Pan's perspective

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

  1. ImageNet Large Scale Visual Recognition Challenge
  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. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
  5. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
  6. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
  7. Training Compute-Optimal Large Language Models (Advances in Neural Information Processing Systems 35)
  8. Training language models to follow instructions with human feedback (Neural Information Processing Systems)
  9. Flamingo: a Visual Language Model for Few-Shot Learning (Neural Information Processing Systems)
  10. Masked Autoencoders Are Scalable Vision Learners (Computer Vision and Pattern Recognition)

Their papers