mlsanityData to 7 Oct 2026

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

arXiv 2508.18445 ↗First posted 25 Aug 2025cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 52 references that are in the map. First posted on arXiv 25 Aug 2025.

Builds on

27 of 52 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Resultscited by 5
  2. 2025VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Resultscited by 0
  3. 2025A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Losscited by 0
  4. 2026Employing Vision-Language Models for Face Image Quality Assessmentcited by 0