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20172022
most citedFacial Makeup Transfer Combining Illumination Transfer

7 citations · 16 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CV20221 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…

cs.CV20212 cited

Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies

Xin Jin, Zhonglan Li, Ke Liu +6

In industry, there exist plenty of scenarios where old gray photos need to be automatically colored, such as video sites and archives. In this paper, we present the HistoryNet focu…

cs.CV2020

A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction

Xin Jin, Xiqiao Li, Heng Huang +2

The task of aesthetic quality assessment is complicated due to its subjectivity. In recent years, the target representation of image aesthetic quality has changed from a one-dimens…

cs.CV2019

Aesthetic Attributes Assessment of Images

Xin Jin, Le Wu, Geng Zhao +6

Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describ…

cs.CV20197 cited

Facial Makeup Transfer Combining Illumination Transfer

Xin Jin, Rui Han, Ning Ning +2

To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software. The makeup effec…

cs.CV2017

Single Reference Image based Scene Relighting via Material Guided Filtering

Xin Jin, Yannan Li, Ningning Liu +4

Image relighting is to change the illumination of an image to a target illumination effect without known the original scene geometry, material information and illumination conditio…