IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring

• URL: http://arxiv.org/abs/2605.24553v1
• Date: Sat, 23 May 2026 12:39:37 GMT
• Title: IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring
• Abstract summary: We present IQA-Spider, the first image quality assessment (IQA) framework that unifies reasoning, grounding, and referring into a single LMM-based framework for multi-granularity quality understanding.
• Score: 17.642234624165685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present IQA-Spider, the first image quality assessment (IQA) framework that unifies reasoning, grounding, and referring into a single LMM-based framework for multi-granularity quality understanding. Existing LMM-based IQA methods typically support only partial perception dimensions, such as quality description and question answering~(\textit{i.e.}, reasoning) or pixel-level grounding. This limitation largely stems from the absence of (i) a unified task and data formulation and (ii) effective optimization paradigms for multi-granularity learning. To address these limitations, we formulate a rigorous four-task paradigm covering global and local quality description, pixel-level grounding, and region-level referring. Based on this formulation, we construct a corresponding IQA dataset with a scalable and automatic annotation pipeline, thereby providing a solid foundation for unified multi-granularity learning. To further enable unified perception, we adopt a conflict-free two-stage design that progressively extends text-level multi-granularity understanding to pixel-level grounding: (i) the first stage equips the model with fine-grained text-level reasoning across multiple IQA tasks, and (ii) the second stage introduces a training-free text-to-point grounding paradigm, which bridges textual semantics and pixel-level perception by mapping token logits to spatial coordinates. Based on these efforts, we achieve IQA-Spider with unified multi-granularity explainable image quality assessment. Extensive experiments across multiple benchmarks demonstrate strong performance, validating the effectiveness and versatility of the proposed formulation and framework.

Related papers

• Text-Visual Semantic Constrained AI-Generated Image Quality Assessment [47.575342788480505]
  We propose a unified framework to enhance the comprehensive evaluation of both text-image consistency and perceptual distortion in AI-generated images.<n>Our approach integrates key capabilities from multiple models and tackles the aforementioned challenges by introducing two core modules.<n>Tests conducted on multiple benchmark datasets demonstrate that SC-AGIQA outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-14T16:21:05Z)
• BPCLIP: A Bottom-up Image Quality Assessment from Distortion to Semantics Based on CLIP [18.25854559825818]
  We propose a bottom-up image quality assessment approach based on the Contrastive Language-Image Pre-training (CLIP)<n>Specifically, we utilize an encoder to extract multiscale features from the input image and introduce a bottom-up multiscale cross attention module.<n>By incorporating 40 image quality adjectives across six distinct dimensions, we enable the pre-trained CLIP text encoder to generate representations of the intrinsic quality of the image.
  arXiv  Detail & Related papers  (2025-06-22T09:56:57Z)
• Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning [49.07442840323135]
  We propose a new paradigm for perception-oriented instruction tuning, i.e., Q-Adapt.<n>Our proposed Q-Adapt can achieve a lightweight visual quality evaluator, demonstrating comparable performance.
  arXiv  Detail & Related papers  (2025-04-02T12:02:57Z)
• Teaching LMMs for Image Quality Scoring and Interpreting [71.1335005098584]
  We propose Q-SiT (Quality Scoring and Interpreting joint Teaching), a unified framework that enables image quality scoring and interpreting simultaneously.<n>Q-SiT is the first model capable of simultaneously performing image quality scoring and interpreting tasks, along with its lightweight variant, Q-SiT-mini.<n> Experimental results demonstrate that Q-SiT achieves strong performance in both tasks with superior generalization IQA abilities.
  arXiv  Detail & Related papers  (2025-03-12T09:39:33Z)
• M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment [65.3860007085689]
  M3-AGIQA is a comprehensive framework that enables more human-aligned, holistic evaluation of AI-generated images.<n>By aligning model outputs more closely with human judgment, M3-AGIQA delivers robust and interpretable quality scores.
  arXiv  Detail & Related papers  (2025-02-21T03:05:45Z)
• Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment [76.77693558769934]
  We introduce a new image quality assessment (IQA) task paradigm, **grounding-IQA**.<n>This paradigm integrates multimodal referring and grounding with IQA to realize more fine-grained quality perception.<n>We develop a well-designed benchmark, GIQA-Bench. The benchmark evaluates the grounding-IQA performance from three perspectives: description quality, VQA accuracy, and grounding precision.
  arXiv  Detail & Related papers  (2024-11-26T09:03:16Z)
• Q-Ground: Image Quality Grounding with Large Multi-modality Models [61.72022069880346]
  We introduce Q-Ground, the first framework aimed at tackling fine-scale visual quality grounding. Q-Ground combines large multi-modality models with detailed visual quality analysis. Central to our contribution is the introduction of the QGround-100K dataset.
  arXiv  Detail & Related papers  (2024-07-24T06:42:46Z)
• UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment [47.207432606050105]
  Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal.<n>We propose Unified vision-language pre-training of Quality and Aesthetics (UniQA) to extract useful and common representations from two tasks.
  arXiv  Detail & Related papers  (2024-06-03T07:40:10Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models [28.194638379354252]
  We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods. DepictQA allows for detailed, language-based, human-like evaluation of image quality by leveraging Multi-modal Large Language Models. These results showcase the research potential of multi-modal IQA methods.
  arXiv  Detail & Related papers  (2023-12-14T14:10:02Z)
• Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective [93.56647950778357]
  Blind image quality assessment (BIQA) predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks.
  arXiv  Detail & Related papers  (2023-03-27T07:58:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.