The rapid advancement of AI-driven visual generation technologies has catalyzed significant breakthroughs in image manipulation, particularly in achieving photorealistic localized editing effects on natural scene images (NSIs). Despite extensive research on image quality assessment (IQA) for AI-generated images (AGIs), most studies focus on fully AI-generated outputs (e.g., text-to-image generation), leaving the quality assessment of partial-AIGC images (PAIs)-images with localized AI-driven edits an almost unprecedented field. Motivated by this gap, we construct the first large-scale PAI dataset towards explainable partial-AIGC image quality assessment (EPAIQA), the EPAIQA-15K, which includes 15K images with localized AI manipulation in different regions and over 300K multi-dimensional human ratings. Based on this, we leverage large multi-modal models (LMMs) and propose a three-stage model training paradigm. This paradigm progressively trains the LMM for editing region grounding, quantitative quality scoring, and quality explanation. Finally, we develop the EPAIQA series models, which possess explainable quality feedback capabilities. Our work represents a pioneering effort in the perceptual IQA field for comprehensive PAI quality assessment.
\underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has gained widespread attention with the increasing efficiency of deep learning in content creation. AIGC, created with the assistance of artificial intelligence technology, includes various forms of content, among which the AI-generated images (AGIs) have brought significant impact to society and have been applied to various fields such as entertainment, education, social media, etc. However, due to hardware limitations and technical proficiency, the quality of AIGC images (AGIs) varies, necessitating refinement and filt…
AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence
Shanghai Jiao Tong University（上海交通大学）；Tianjin University（天津大学）；Shanghai Second Polytechnic University（上海第二工业大学）
Technical University of Denmark（丹麦技术大学）；Medable A/S（Medable公司）；University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital（哥本哈根大学医院）
Image quality is a crucial factor in the effectiveness and efficiency of teledermatological consultations. However, up to 50% of images sent by patients have quality issues, thus increasing the time to diagnosis and treatment. An automated, easily deployable, explainable method for assessing image quality is necessary to improve the current teledermatological consultation flow. We introduce ImageQX, a convolutional neural network for image quality assessment with a learning mechanism for identifying the most common poor image quality explanations: bad framing, bad lighting, blur, low resolutio…
Norwegian University of Science and Technology（挪威科技大学）；Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究所）；Technical University of Darmstadt（达姆施塔特工业大学）
An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of making face recognition processes understandable for humans. In this work, we introduce the concept of pixel-level face image quality that determines the utility of pixels in a face image for recognition. We propose a training-free approach to assess the pixel-level qualities of a face image given an arbitrary face recognition network. To achieve this, a model-specific quality value of the input image is estimated an…
Tsinghua University（清华大学）；BNRist；OPPO AI Center（OPPO人工智能中心）；Peking University（北京大学）；Hangzhou Zhuoxi Institute of Brain and Intelligence（杭州智行脑科学与人工智能研究所）
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（维也纳医科大学医学数据科学中心人工智能研究所）；Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria（维也纳医科大学视网膜人工智能克里斯蒂安·多普勒实验室）
Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment
Cardiff University（卡迪夫大学）；Shandong University of Science and Technology（山东科技大学）；Shenzhen University（深圳大学）；Sun Yat-sen University（中山大学）；City University of Hong Kong（香港城市大学）
VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Yale University（耶鲁大学）
Medical image quality assessment is an important aspect of image acquisition, as poor-quality images may lead to misdiagnosis. Manual labelling of image quality is a tedious task for population studies and can lead to misleading results. While much research has been done on automated analysis of image quality to address this issue, relatively little work has been done to explain the methodologies. In this work, we propose an explainable image quality assessment system and validate our idea on two different objectives which are foreign object detection on Chest X-Rays (Object-CXR) and Left Vent…
Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
University of Alabama（阿拉巴马大学）；University of Maryland, College Park（马里兰大学学院公园分校）；Stony Brook University（石溪大学）；University of Florida（佛罗里达大学）；Johns Hopkins University（约翰霍普金斯大学）
Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery
K.N. Toosi University of Technology（霍加托西大学）；Faculties of Electrical and Computer Engineering（电气与计算机工程学院）；Applied Robotics and AI Solutions (ARAS)（应用机器人与人工智能解决方案（ARAS））
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（图像通信与网络工程研究所，上海交通大学）；Tianjin University（天津大学）；East China Normal University（华东师范大学）；Polytech Nantes, Université de Nantes（南特理工大学）
EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ant Group（蚂蚁集团）
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）
Not only automation of manufacturing processes but also automation of automation procedures itself become increasingly relevant to automation research. In this context, automated capability assessment, mainly leveraged by deep learning systems driven from 3D CAD data, have been presented. Current assessment systems may be able to assess CAD data with regards to abstract features, e.g. the ability to automatically separate components from bulk goods, or the presence of gripping surfaces. Nevertheless, they suffer from the factor of black box systems, where an assessment can be learned and gener…
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸式媒体技术重点实验室）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）
