ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking

• URL: http://arxiv.org/abs/2603.20785v1
• Date: Sat, 21 Mar 2026 12:23:08 GMT
• Title: ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking
• Abstract summary: We introduce ME-IQA, a test-time memory-enhanced re-ranking framework.<n>It builds a memory bank and retrieves semantically and perceptually aligned neighbors using reasoning summaries.<n>It performs gated reflection and consolidates memory to improve future decisions.
• Score: 25.561100902733326
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reasoning-induced vision-language models (VLMs) advance image quality assessment (IQA) with textual reasoning, yet their scalar scores often lack sensitivity and collapse to a few values, so-called discrete collapse. We introduce ME-IQA, a plug-and-play, test-time memory-enhanced re-ranking framework. It (i) builds a memory bank and retrieves semantically and perceptually aligned neighbors using reasoning summaries, (ii) reframes the VLM as a probabilistic comparator to obtain pairwise preference probabilities and fuse this ordinal evidence with the initial score under Thurstone's Case V model, and (iii) performs gated reflection and consolidates memory to improve future decisions. This yields denser, distortion-sensitive predictions and mitigates discrete collapse. Experiments across multiple IQA benchmarks show consistent gains over strong reasoning-induced VLM baselines, existing non-reasoning IQA methods, and test-time scaling alternatives.

Related papers

• Harnessing Image Question Dependence for Better VLM Test-time Reinforcement Learning [66.13367818892503]
  We propose TTIQ, a test-time reinforcement learning framework that harnesses image-question dependence for better vlm adaptation.<n> TTIQ teacher-forces each sampled response under the original image-question pair and its image- and question-ablated variants.<n>It combines image and question dependence with confidence to construct a response-level reward that favors jointly grounded responses.
  arXiv  Detail & Related papers  (2026-09-09T22:54:15Z)
• IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment [59.83941105390897]
  We propose IQA-T1, a tool-based visual evidence reasoning framework.<n>During inference, the model autonomously invokes specialized analysis tools to generate structured visual evidence.<n>Experiments show that IQA-T1 achieves the best overall performance across datasets.
  arXiv  Detail & Related papers  (2026-07-14T05:47:32Z)
• MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment [20.4401121122447]
  Blind image quality assessment (BIQA) is commonly built on two basic learning paradigms: regression and ranking.<n>In this work, we revisit what underlies regression and ranking and identify pairwise relational distance, termed quality margin, as their common bridge.<n>Motivated by this insight, we propose MR-IQA, a direct quality-margin optimization framework for reinforcement learning (RL)-based BIQA.
  arXiv  Detail & Related papers  (2026-06-29T04:07:46Z)
• Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement [58.15004031934379]
  We propose Q-DeepSight, a think-with-image framework that emulates this human-like process.<n>Q-DeepSight achieves state-of-the-art performance across diverse benchmarks, including natural, restored, and AI-generated content.<n>We demonstrate its practical value with Perceptual-in-Generation (PiG), a training-free framework where Q-DeepSight's diagnoses guide iterative image enhancement.
  arXiv  Detail & Related papers  (2026-04-18T06:10:57Z)
• Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation [47.96044455071274]
  V-QPP-Bench is the first benchmark dedicated to Visual Query Pre-processing.<n>Visual imperfections severely degrade both retrieval recall and end-to-end MRAG performance.<n>Off-the-shelf MLLMs struggle with tool selection and parameter prediction without specialized training.<n> supervised fine-tuning enables compact models to achieve comparable or superior performance to larger proprietary models.
  arXiv  Detail & Related papers  (2026-02-13T18:39:48Z)
• ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval [64.14282916266998]
  Composed Image Retrieval aims to retrieve target images based on a hybrid query comprising a reference image and a modification text.<n>We propose ReCALL, a model-agnostic framework that follows a diagnose-generate-refine pipeline.<n>Experiments on CIRR and FashionIQ show that ReCALL consistently recalibrates degraded capabilities and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-02T04:52:54Z)
• Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning [32.30800226412995]
  We introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors.<n>We show that Zoom-IQA achieves improved robustness, explainability, and generalization.<n>The application to downstream tasks, such as image restoration, further demonstrates the effectiveness of Zoom-IQA.
  arXiv  Detail & Related papers  (2026-01-06T11:00:17Z)
• Revisiting MLLM Based Image Quality Assessment: Errors and Remedy [23.918454005000328]
  A key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks.<n>We propose Q-Scorer, which incorporates a lightweight regression module and IQA-specific score tokens into the MLLM pipeline.<n>Q-Scorer achieves state-of-the-art performance across multiple IQA benchmarks, generalizes well to mixed datasets, and further improves when combined with other methods.
  arXiv  Detail & Related papers  (2025-11-11T04:08:44Z)
• Uncertainty Quantification for Regression using Proper Scoring Rules [76.24649098854219]
  We introduce a unified UQ framework for regression based on proper scoring rules, such as CRPS, logarithmic, squared error, and quadratic scores.<n>We derive closed-form expressions for the uncertainty measures under practical parametric assumptions and show how to estimate them using ensembles of models.<n>Our broad evaluation on synthetic and real-world regression datasets provides guidance for selecting reliable UQ measures.
  arXiv  Detail & Related papers  (2025-09-30T17:52:12Z)
• AgenticIQA: An Agentic Framework for Adaptive and Interpretable Image Quality Assessment [69.06977852423564]
  Image quality assessment (IQA) reflects both the quantification and interpretation of perceptual quality rooted in the human visual system.<n>AgenticIQA decomposes IQA into four subtasks -- distortion detection, distortion analysis, tool selection, and tool execution.<n>To support training and evaluation, we introduce AgenticIQA-200K, a large-scale instruction dataset tailored for IQA agents, and AgenticIQA-Eval, the first benchmark for assessing the planning, execution, and summarization capabilities of VLM-based IQA agents.
  arXiv  Detail & Related papers  (2025-09-30T09:37:01Z)
• VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning [50.34205095371895]
  Video quality assessment aims to objectively quantify perceptual quality degradation.<n>Existing VQA models suffer from two critical limitations.<n>We propose textbfVQAThinker, a reasoning-based VQA framework.
  arXiv  Detail & Related papers  (2025-08-08T06:16:23Z)
• SoftPQ: Robust Instance Segmentation Evaluation via Soft Matching and Tunable Thresholds [0.0]
  We propose SoftPQ, a flexible and interpretable instance segmentation metric.<n>We show that SoftPQ captures meaningful differences in segmentation quality that existing metrics overlook.
  arXiv  Detail & Related papers  (2025-05-17T22:08:33Z)
• HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation [20.178644251662316]
  We introduce the hierarchical graph of thoughts (HGOT) to enhance the retrieval of pertinent passages during in-context learning. The framework employs the divide-and-conquer strategy to break down complex queries into manageable sub-queries. It refines self-consistency majority voting for answer selection, which incorporates the recently proposed citation recall and precision metrics.
  arXiv  Detail & Related papers  (2024-02-14T18:41:19Z)

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.