Pith. sign in

REVIEW

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2508.18445 v1 pith:46OIKBSH submitted 2025-08-25 cs.CV

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Sizhuo Ma , Wei-Ting Chen , Qiang Gao , Jian Wang , Chris Wei Zhou , Wei Sun , Weixia Zhang , Linhan Cao
show 51 more authors
Jun Jia Xiangyang Zhu Dandan Zhu Xiongkuo Min Guangtao Zhai Baoying Chen Xiongwei Xiao Jishen Zeng Wei Wu Tiexuan Lou Yuchen Tan Chunyi Song Zhiwei Xu MohammadAli Hamidi Hadi Amirpour Mingyin Bai Jiawang Du Zhenyu Jiang Zilong Lu Ziguan Cui Zongliang Gan Xinpeng Li Shiqi Jiang Chenhui Li Changbo Wang Weijun Yuan Zhan Li Yihang Chen Yifan Deng Ruting Deng Zhanglu Chen Boyang Yao Shuling Zheng Feng Zhang Zhiheng Fu Abhishek Joshi Aman Agarwal Rakhil Immidisetti Ajay Narasimha Mopidevi Vishwajeet Shukla Hao Yang Ruikun Zhang Liyuan Pan Kaixin Deng Hang Ouyang Fan yang Zhizun Luo Zhuohang Shi Songning Lai Weilin Ruan Yutao Yue
This is my paper
classification cs.CV
keywords facechallengeimageimagesqualityassessmentdegradationsfiqa
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.