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Hao Lou

Beijing Electronic Science and Technology Institute

4 papers hereh-index 453 citations8 works total

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

author position
  • middle author4

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

fields
  • cs.CV4
affiliations
  • Beijing Electronic Science and Technology Institute
ORCID 0000-0003-0148-1574
same name
  • Hao Lou — 6 papers, h 8
  • Hao Lou — 3 papers, h 5
  • Hao Lou — 1 paper, h 1
  • Hao Lou — 1 paper, 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 citedAesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

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

collaborators

4 papers

cs.CV2023

Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels

Heng Huang, Xin Jin, Yaqi Liu +5

Now many mobile phones embed deep-learning models for evaluation or guidance on photography. These models cannot provide detailed results like human pose scores or scene color scor…

cs.CV2022

Aesthetic Language Guidance Generation of Images Using Attribute Comparison

Xin Jin, Qiang Deng, Jianwen Lv +3

With the vigorous development of mobile photography technology, major mobile phone manufacturers are scrambling to improve the shooting ability of equipments and the photo beautifi…

cs.CV2022★ 2 cited

Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

Xin Jin, Xinning Li, Hao Lou +5

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evalu…

cs.CV2022★ 1 cited

Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment

Xin Jin, Hao Lou, Huang Heng +4

In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To re…

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