no code implementations • 4 Jun 2024 • Xinghui Zhou, Wenbo Zhou, Tianyi Wei, Shen Chen, Taiping Yao, Shouhong Ding, Weiming Zhang, Nenghai Yu
Extensive experiments confirm the superiority of our method over existing general no-reference image quality assessment metrics and the latest metric of facial image quality assessment, making it well suited for evaluating face swapping images in real-world scenarios.
no code implementations • 10 Aug 2022 • Xin Jin, Wu Zhou, Xinghui Zhou, Shuai Cui, Le Zhang, Jianwen Lv, Shu Zhao
In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images.
no code implementations • 9 Aug 2022 • Xinghui Zhou, Xin Jin, Jianwen Lv, Heng Huang, Ming Mao, Shuai Cui
In this paper, we propose aesthetic attribute assessment, which is the aesthetic attributes captioning, i. e., to assess the aesthetic attributes such as composition, lighting usage and color arrangement.
no code implementations • 15 Oct 2020 • Xin Jin, Xiqiao Li, Heng Huang, XiaoDong Li, Xinghui Zhou
In this paper, we propose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predict aesthetic score distribution from images.
2 code implementations • 11 Jul 2019 • Xin Jin, Le Wu, Geng Zhao, Xiao-Dong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.