Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
Recent studies demonstrate that multimodal large language models (MLLMs) can
proficiently evaluate visual quality through interpretable assessments.
However, existing approaches typically treat quality scoring and reasoning
descriptions as separate tasks with disjoint optimization objectives, leading
to a trade-off: models adept at quality reasoning descriptions struggle with
precise score regression, while score-focused models lack interpretability.
This limitation hinders the full potential of MLLMs in visual quality
assessment, where accuracy and interpretability should be mutually reinforcing.
To address this, we propose a unified two-stage training framework comprising a
cold-start stage and a reinforcement learning-based fine-tuning stage.
Specifically, in the first stage, we distill high-quality data from a teacher
model through expert-designed prompts, initializing reasoning capabilities via
cross-entropy loss supervision. In the second stage, we introduce a novel
reward with Group Relative Policy Optimization (GRPO) to jointly optimize
scoring accuracy and reasoning consistency. We designate the models derived
from these two stages as Q-Ponder-CI and Q-Ponder. Extensive experiments show
that Q-Ponder achieves state-of-the-art (SOTA) performance on quality score
regression benchmarks, delivering up to 6.5% higher SRCC on cross-domain
datasets. Furthermore, Q-Ponder significantly outperforms description-based
SOTA models, including its teacher model Qwen-2.5-VL-72B, particularly in
description accuracy and reasonableness, demonstrating the generalization
potential over diverse tasks.
