Q-DeepSight：利用图像激励思考用于图像质量评估与细化
Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement
AI总结：
本文提出Q-DeepSight框架，通过多模态链式思考与工具增强证据获取，提升图像质量评估与细化的准确性与可靠性。
摘要：
图像质量评估（IQA）模型日益被用作感知批评者，以指导生成模型和图像修复。这一角色不仅需要准确的评分，还需要可操作的局部反馈。然而，当前基于大语言模型的方法采用单一视角、仅语言的范式，偏离人类证据寻求判断，导致推理依据薄弱，限制了其在闭环细化中的可靠性。本文提出Q-DeepSight，一种“图像激励思考”的框架，通过交错的多模态链式思考（iMCoT）与工具增强证据获取（如裁剪和缩放）来明确确定质量退化的位置和原因。为了通过强化学习训练这些长iMCoT轨迹，我们引入了两种技术：感知课程奖励（PCR）以缓解奖励稀疏性，以及证据梯度过滤（EGF）以改进对视觉基础推理的信用分配。Q-DeepSight在多样化的基准测试中均取得最佳性能，包括自然、修复和AI生成内容。此外，我们通过Perceptual-in-Generation（PiG）展示了其实际价值，这是一种无需训练的框架，其中Q-DeepSight的诊断指导迭代图像增强，有效闭合评估与细化之间的闭环。
英文摘要：
Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demands not only accurate scores but also actionable, localized feedback. However, current MLLM-based methods adopt a single-look, language-only paradigm, which departs from human evidence-seeking judgment and yields weakly grounded rationales, limiting their reliability for in-the-loop refinement. We propose Q-DeepSight, a think-with-image framework that emulates this human-like process. It performs interleaved Multimodal Chain-of-Thought (iMCoT) with tool-augmented evidence acquisition (e.g., crop-and-zoom) to explicitly determine where quality degrades and why. To train these long iMCoT trajectories via reinforcement learning, we introduce two techniques: Perceptual Curriculum Reward (PCR) to mitigate reward sparsity and Evidence Gradient Filtering (EGF) to improve credit assignment for visually-grounded reasoning. Q-DeepSight achieves state-of-the-art performance across diverse benchmarks, including natural, restored, and AI-generated content. Furthermore, we demonstrate its practical value with Perceptual-in-Generation (PiG), a training-free framework where Q-DeepSight's diagnoses guide iterative image enhancement, effectively closing the loop between assessment and refinement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
