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Qi Dai

26 papers hereh-index 305.2k citations61 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author25

Across the 25 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • cs.LG1
same name
  • Qi Dai — 22 papers, h 7
  • Qi Dai — 9 papers, h 4
  • Qi Dai — 7 papers, h 4
  • Qi Dai — 7 papers, h 6
  • Qi Dai — 4 papers, h 5
  • Qi Dai — 3 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedSelf-Supervised Learning with Swin Transformers

113 citations · 337 across the 24 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 15 cited

SimMIM: A Simple Framework for Masked Image Modeling

Zhenda Xie, Zheng Zhang, Yue Cao +5

This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and to…

cs.CV2021★ 5 cited

Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning

Shaobo Min, Qi Dai, Hongtao Xie +3

Cross-modal correlation provides an inherent supervision for video unsupervised representation learning. Existing methods focus on distinguishing different video clips by visual an…

cs.CV2021★ 71 cited

On the Connection between Local Attention and Dynamic Depth-wise Convolution

Qi Han, Zejia Fan, Qi Dai +4

Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in L…

cs.CV2021★ 113 cited

Self-Supervised Learning with Swin Transformers

Zhenda Xie, Yutong Lin, Zhuliang Yao +4

We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers a…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.