Q-Ground: Image Quality Grounding with Large Multi-modality Models

• URL: http://arxiv.org/abs/2407.17035v1
• Date: Wed, 24 Jul 2024 06:42:46 GMT
• Title: Q-Ground: Image Quality Grounding with Large Multi-modality Models
• Authors: Chaofeng Chen, Sensen Yang, Haoning Wu, Liang Liao, Zicheng Zhang, Annan Wang, Wenxiu Sun, Qiong Yan, Weisi Lin,
• Abstract summary: 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.
• Score: 61.72022069880346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual content. However, these advancements are mostly focused on overall quality assessment, and the detailed examination of local quality, which is crucial for comprehensive visual understanding, is still largely unexplored. In this work, we introduce Q-Ground, the first framework aimed at tackling fine-scale visual quality grounding by combining large multi-modality models with detailed visual quality analysis. Central to our contribution is the introduction of the QGround-100K dataset, a novel resource containing 100k triplets of (image, quality text, distortion segmentation) to facilitate deep investigations into visual quality. The dataset comprises two parts: one with human-labeled annotations for accurate quality assessment, and another labeled automatically by LMMs such as GPT4V, which helps improve the robustness of model training while also reducing the costs of data collection. With the QGround-100K dataset, we propose a LMM-based method equipped with multi-scale feature learning to learn models capable of performing both image quality answering and distortion segmentation based on text prompts. This dual-capability approach not only refines the model's understanding of region-aware image quality but also enables it to interactively respond to complex, text-based queries about image quality and specific distortions. Q-Ground takes a step towards sophisticated visual quality analysis in a finer scale, establishing a new benchmark for future research in the area. Codes and dataset are available at https://github.com/Q-Future/Q-Ground.

Related papers

• VQA$^2$: Visual Question Answering for Video Quality Assessment [76.81110038738699]
  Video Quality Assessment (VQA) is a classic field in low-level visual perception. Recent studies in the image domain have demonstrated that Visual Question Answering (VQA) can enhance markedly low-level visual quality evaluation. We introduce the VQA2 Instruction dataset - the first visual question answering instruction dataset that focuses on video quality assessment. The VQA2 series models interleave visual and motion tokens to enhance the perception of spatial-temporal quality details in videos.
  arXiv  Detail & Related papers  (2024-11-06T09:39:52Z)
• Descriptive Image Quality Assessment in the Wild [25.503311093471076]
  VLM-based Image Quality Assessment (IQA) seeks to describe image quality linguistically to align with human expression. We introduce Depicted image Quality Assessment in the Wild (DepictQA-Wild) Our method includes a multi-functional IQA task paradigm that encompasses both assessment and comparison tasks, brief and detailed responses, full-reference and non-reference scenarios.
  arXiv  Detail & Related papers  (2024-05-29T07:49:15Z)
• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• Enhancing Blind Video Quality Assessment with Rich Quality-aware Features [79.18772373737724]
  We present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. We explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features. Experimental results demonstrate that the proposed model achieves the best performance on three public social media VQA datasets.
  arXiv  Detail & Related papers  (2024-05-14T16:32:11Z)
• 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)
• IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models [23.99102775778499]
  This paper introduces IQAGPT, an innovative image quality assessment system integrating an image quality captioning VLM with ChatGPT. We build a CT-IQA dataset for training and evaluation, comprising 1,000 CT slices with diverse quality levels professionally annotated. To better leverage the capabilities of LLMs, we convert annotated quality scores into semantically rich text descriptions using a prompt template.
  arXiv  Detail & Related papers  (2023-12-25T09:13:18Z)
• Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment [7.291687946822539]
  A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations. We enable the learning of low-level quality features to distortion types by introducing a novel quality-aware contrastive loss. We design zero-shot quality predictions from both pathways in a completely blind setting.
  arXiv  Detail & Related papers  (2023-12-08T05:24:21Z)
• Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild [38.197794061203055]
  We propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. We deploy the complementary low and high-level image representations obtained from the Re-IQA framework to train a linear regression model. Our method achieves state-of-the-art performance on multiple large-scale image quality assessment databases.
  arXiv  Detail & Related papers  (2023-04-02T05:06:51Z)
• Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images [73.27643795557778]
  Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns. We present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score.
  arXiv  Detail & Related papers  (2023-03-18T09:04:55Z)
• MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion [8.338999282303755]
  We propose a novel algorithm based on the Swin Transformer. It aggregates information from both local and global features to better predict the quality. It ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge.
  arXiv  Detail & Related papers  (2022-05-20T11:34:35Z)

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.