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Jiajun Liang

4 papers here

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

author position
  • first author1
  • middle author2
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
ORCID 0000-0002-1100-4731
same name
  • Jiajun Liang — 9 papers, h 6
  • Jiajun Liang — 8 papers, h 5
  • Jiajun Liang — 5 papers, h 3
  • Jiajun Liang — 4 papers, h 4
  • Jiajun Liang — 3 papers, h 12
  • Jiajun Liang — 3 papers, h 2

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

most citedEfficient One Pass Self-distillation with Zipf's Label Smoothing

3 citations · 5 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 1 cited

Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers

Siyuan Wei, Tianzhu Ye, Shen Zhang +2

Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previ…

cs.CV2023★ 1 cited

DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior

Shuangping Jin, Bingbing Yu, Minhao Jing +3

RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images…

cs.CV2022★ 3 cited

Efficient One Pass Self-distillation with Zipf's Label Smoothing

Jiajun Liang, Linze Li, Zhaodong Bing +4

Self-distillation exploits non-uniform soft supervision from itself during training and improves performance without any runtime cost. However, the overhead during training is ofte…

cs.CV2022

Explaining Deepfake Detection by Analysing Image Matching

Shichao Dong, Jin Wang, Jiajun Liang +2

This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspecti…

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