Segmenting and Understanding: Region-aware Semantic Attention for Fine-grained Image Quality Assessment with Large Language Models

• URL: http://arxiv.org/abs/2508.07818v1
• Date: Mon, 11 Aug 2025 10:03:00 GMT
• Title: Segmenting and Understanding: Region-aware Semantic Attention for Fine-grained Image Quality Assessment with Large Language Models
• Authors: Chenyue Song, Chen Hui, Haiqi Zhu, Feng Jiang, Yachun Mi, Wei Zhang, Shaohui Liu,
• Abstract summary: No-reference image quality assessment (NR-IQA) aims to simulate the process of perceiving image quality aligned with human perception.<n>We propose a fine-grained image quality assessment model, named RSFIQA, which integrates region-level distortion information to perceive multi-dimensional quality discrepancies.
• Score: 15.08436609204683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: No-reference image quality assessment (NR-IQA) aims to simulate the process of perceiving image quality aligned with subjective human perception. However, existing NR-IQA methods either focus on global representations that leads to limited insights into the semantically salient regions or employ a uniform weighting for region features that weakens the sensitivity to local quality variations. In this paper, we propose a fine-grained image quality assessment model, named RSFIQA, which integrates region-level distortion information to perceive multi-dimensional quality discrepancies. To enhance regional quality awareness, we first utilize the Segment Anything Model (SAM) to dynamically partition the input image into non-overlapping semantic regions. For each region, we teach a powerful Multi-modal Large Language Model (MLLM) to extract descriptive content and perceive multi-dimensional distortions, enabling a comprehensive understanding of both local semantics and quality degradations. To effectively leverage this information, we introduce Region-Aware Semantic Attention (RSA) mechanism, which generates a global attention map by aggregating fine-grained representations from local regions. In addition, RSFIQA is backbone-agnostic and can be seamlessly integrated into various deep neural network architectures. Extensive experiments demonstrate the robustness and effectiveness of the proposed method, which achieves competitive quality prediction performance across multiple benchmark datasets.

Related papers

• AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image [61.54860340942449]
  We introduce a novel concept of anchor instance (AI), a compact subset of instances that are representative within their regions (local) and discriminative at the bag (global) level.<n>These AIs act as semantic references to guide interactions across regions, correcting non-discriminative patterns while preserving regional diversity.<n>We develop a concise yet effective framework, AINet, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters.
  arXiv  Detail & Related papers  (2026-02-21T09:36:27Z)
• DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment [9.851063768646847]
  A long-held challenge in no-reference image quality assessment is the lack of objective generalization to unseen natural distortions.<n>We integrate a novel Depth-Guided cross-attention and refinement mechanism, which distills scene depth and spatial features into a structure-aware representation.<n>We implement TCB and Depth-CAR as multimodal attention-based projection functions to select the most informative features.<n> Experimental results demonstrate that our proposed DGIQA model achieves state-of-the-art (SOTA) performance on both synthetic and authentic benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-29T20:52:56Z)
• 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)
• Local and Global Feature Attention Fusion Network for Face Recognition [6.479154287764899]
  We propose a Local and Global Feature Attention Fusion (LGAF) network based on feature quality.<n>The network adaptively allocates attention between local and global features according to feature quality and obtains more discriminative and high-quality face features.<n> Experimental results demonstrate that the LGAF achieves the best average performance on $4$ validation sets.
  arXiv  Detail & Related papers  (2024-11-25T07:55:57Z)
• 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)
• Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization [30.92656780805478]
  We propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for fine-grained visual categorization (FGVC) To model the spatial contextual relationship between rich part descriptors and global semantics, we develop a novel multi-part and multi-scale cross-attention (MPMSCA) module. We also propose a generic multi-level semantic quality evaluation module (MLSQE) to progressively supervise and enhance hierarchical semantics from different levels of the backbone network.
  arXiv  Detail & Related papers  (2024-03-15T13:40:44Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Recognize Any Regions [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model.<n>Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment [53.72721476803585]
  Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. We propose a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions. A key component of our approach is the proposed cross-scale attention mechanism, which calculates attention maps for lower level features.
  arXiv  Detail & Related papers  (2023-08-06T09:08:37Z)
• R-MAE: Regions Meet Masked Autoencoders [113.73147144125385]
  We explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-training baseline, we propose masked region autoencoding to learn from groups of pixels or regions.
  arXiv  Detail & Related papers  (2023-06-08T17:56:46Z)
• Region-Based Semantic Factorization in GANs [67.90498535507106]
  We present a highly efficient algorithm to factorize the latent semantics learned by Generative Adversarial Networks (GANs) concerning an arbitrary image region. Through an appropriately defined generalized Rayleigh quotient, we solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-02-19T17:46:02Z)
• ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification [11.680355561258427]
  High-resolution images hinder progress in digital pathology. patch-based processing often incorporates multiple instance learning (MIL) to aggregate local patch-level representations yielding image-level prediction. This paper proposes a transformer-based architecture specifically tailored for histological image classification. It combines fine-grained local attention with a coarse global attention mechanism to learn meaningful representations of high-resolution images at an efficient computational cost.
  arXiv  Detail & Related papers  (2022-02-15T16:55:09Z)

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.