iDETEX: Empowering MLLMs for Intelligent DETailed EXplainable IQA

TL;DR

iDETEX is a novel multimodal large language model that provides detailed, explainable image quality assessments by integrating multiple tasks and advanced training strategies, achieving state-of-the-art results on large-scale benchmarks.

Contribution

The paper introduces iDETEX, a unified MLLM capable of simultaneous quality grounding, perception, and description, with innovative training modules for improved generalization and interpretability.

Findings

Achieves state-of-the-art performance on ViDA-UGC benchmark.

Ranks first in the ICCV MIPI 2025 Detailed Image Quality Assessment Challenge.

Demonstrates robustness and effectiveness in detailed, explainable IQA.

Abstract

Image Quality Assessment (IQA) has progressed from scalar quality prediction to more interpretable, human-aligned evaluation paradigms. In this work, we address the emerging challenge of detailed and explainable IQA by proposing iDETEX-a unified multimodal large language model (MLLM) capable of simultaneously performing three key tasks: quality grounding, perception, and description. To facilitate efficient and generalizable training across these heterogeneous subtasks, we design a suite of task-specific offline augmentation modules and a data mixing strategy. These are further complemented by online enhancement strategies to fully exploit multi-sourced supervision. We validate our approach on the large-scale ViDA-UGC benchmark, where iDETEX achieves state-of-the-art performance across all subtasks. Our model ranks first in the ICCV MIPI 2025 Detailed Image Quality Assessment Challenge, demonstrating its effectiveness and robustness in delivering accurate and interpretable quality assessments.