The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two perspectives: overall quality explanation, and attribute-wise perception answering. However, existing works usually overlooked the conflicts between these two types of perception explanations during joint instruction tuning, leading to insufficient perception understanding. To mitigate this, we propose a new paradigm for perception-oriented instruction tuning, i.e., Q-Adapt, which aims to eliminate the conflicts and achieve the synergy between these two EIQA tasks when adapting LMM, resulting in enhanced multi-faceted explanations of IQA. Particularly, we propose a progressive instruction tuning strategy by dividing the adaption process of LMM for EIQA into two stages, where the first stage empowers the LMM with universal perception knowledge tailored for two tasks using an efficient transfer learning strategy, i.e., LoRA, and the second stage introduces the instruction-adaptive visual prompt tuning to dynamically adapt visual features for the different instructions from two tasks. In this way, our proposed Q-Adapt can achieve a lightweight visual quality evaluator, demonstrating comparable performance and, in some instances, superior results across perceptual-related benchmarks and commonly-used IQA databases. The source code is publicly available at https://github.com/yeppp27/Q-Adapt.

翻译：大型多模态基础模型（LMM）的快速发展为通过指令调适实现可解释图像质量评估（EIQA）开辟了道路，这主要从两个视角展开：整体质量解释与基于属性的感知问答。然而，现有研究通常在联合指令调适过程中忽视了这两类感知解释之间的冲突，导致对感知的理解不够充分。为缓解此问题，我们提出了一种面向感知的指令调适新范式——Q-Adapt，其目标是在调适LMM时消除冲突并实现两项EIQA任务之间的协同，从而增强IQA的多维度解释能力。具体而言，我们提出了一种渐进式指令调适策略，将LMM针对EIQA的调适过程分为两个阶段：第一阶段通过高效的迁移学习策略（即LoRA）赋予LMM适用于两项任务的通用感知知识；第二阶段引入指令自适应的视觉提示调适，以针对两项任务的不同指令动态调整视觉特征。通过这种方式，我们提出的Q-Adapt能够实现一个轻量级的视觉质量评估器，在感知相关基准测试和常用IQA数据库上展现出可比甚至在某些情况下更优的性能。源代码已公开于https://github.com/yeppp27/Q-Adapt。