VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results
This addresses the problem of enhancing visual quality reasoning for LMMs, providing a benchmark for future research, though it is incremental as it builds on existing challenge formats.
The paper summarizes the VQualA 2025 Challenge, which evaluated state-of-the-art Large Multimodal Models (LMMs) on visual quality comparison tasks using a novel benchmark with thousands of coarse-to-fine grained tasks, resulting in around 100 participants and five models demonstrating emerging capabilities in this area.
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 Workshop 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 interpretable and human-aligned quality evaluation systems.