Abstract
This paper presents a summary of the VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models (LMMs), hosted as part of the ICCV 2025 Work-shop on Visual Quality Assessment. The challenge aims to evaluate and enhance the ability of state-of-the-art LMMs to perform open-ended and detailed reasoning about visual quality differences across multiple images. To this end, the competition introduces a novel benchmark comprising thousands of coarse-to-fine grained visual quality comparison tasks, spanning single images, pairs, and multi-image groups. Each task requires models to provide accurate quality judgments. The competition emphasizes holistic evaluation protocols, including 2AFC-based binary preference and multi-choice questions (MCQs). Around 100 participants submitted entries, with five models demonstrating the emerging capabilities of instruction-tuned LMMs on quality assessment. This challenge marks a significant step toward open-domain visual quality reasoning and comparison and serves as a catalyst for future research on inter-pretable and human-aligned quality evaluation systems.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3383-3393 |
| Number of pages | 11 |
| ISBN (Electronic) | 9798331589882 |
| DOIs | |
| State | Published - 2025 |
| Event | 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025 - Honolulu, United States Duration: 19 Oct 2025 → 20 Oct 2025 |
Publication series
| Name | Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025 |
|---|
Conference
| Conference | 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025 |
|---|---|
| Country/Territory | United States |
| City | Honolulu |
| Period | 19/10/25 → 20/10/25 |
Keywords
- Image Quality Assessment
- Large Multimodal Models
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