Image compression has been applied in the fields of image storage and video broadcasting. However, it's formidably tough to distinguish the subtle quality differences between those distorted images generated by different algorithms. In this paper, we propose a new image quality assessment framework to decide which image is better in an image group. To capture the subtle differences, a fine-grained network is adopted to acquire multi-scale features. Subsequently, we design a cross subtract block for separating and gathering the information within positive and negative image pairs. Enabling image comparison in feature space. After that, a progressive feature fusion block is designed, which fuses multi-scale features in a novel progressive way. Hierarchical spatial 2D features can thus be processed gradually. Experimental results show that compared with the current mainstream image quality assessment methods, the proposed network can achieve more accurate image quality assessment and ranks second in the benchmark of CLIC in the image perceptual model track.
Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
School of Software Engineering, Dalian University of Technology（大连理工大学软件学院）；Information Science and Technology College, Dalian Martime University（大连海事大学信息科学与技术学院）
Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；Sofia University（索菲亚大学）；University of Macau（澳门大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen University of Advanced Technology（深圳先进技术大学）
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of Dalian Medical University（大连医科大学附属第一医院）
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the mu…
The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases where images are often captured in low-light conditions (e.g. nighttime) and achieved promising results for enhancing the quality of images. However, they are inferior to handling images under over-exposure. To mitigate this limitation, we propose a novel unsupervised enhancement framework which is robust against various lighting conditions while does not require any well-exposed images to serve as the ground-truths…
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion. Each semantic filter learns a visually interpretable local structure in conjunction with other filters. The significance of learning these interpretable filter sets is demonstrated on two contrasting applications. The first application is image recognition under progressive decolorization, in which recognition algorithms should be color-insensitive to achieve a robust performance. The second application is image quality assessment where objective m…
Respiratory Differencing: Enhancing Pulmonary Thermal Ablation Evaluation Through Pre- and Intra-Operative Image Fusion
Beijing Institute of Respiratory Medicine（北京呼吸病研究所）；Beijing Chao Yang Hospital（北京朝阳医院）；Capital Medical University（首都医科大学）；First Imaging Medical Equipment（第一影像医疗设备公司）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss
University of Oxford（牛津大学）；Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院 NHS 基金会信托基金）；Oxford Radiology Research Unit（牛津放射学研究单元）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；King’s College London（伦敦国王学院）
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms. We present PyTorch Image Quality (PIQ), a usability-centric library that contains the most popular modern IQA algorithms, guaranteed to be correctly implemented according to their original propositions and thoroughly verified. In this paper, we detail the principles behind the foundation of the library, describe the evaluation strategy that makes it reliable, provide the benchmarks that showcase the performan…
Qingdao University（青岛大学）；New York University（纽约大学）；Shandong University（山东大学）；De Montfort University（德蒙福特大学）
With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages. However, the existing point cloud quality assessments ignore the mechanism of human visual system (HVS) which has an important impact on the accuracy of the perceptual quality assessment. In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed. The PKT…
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）
Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
Mashang Consumer Finance Co., Ltd.（马莎消费金融有限公司）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
LGCA: Enhancing Semantic Representation via Progressive Expansion
University of Science, Vietnam National University Ho Chi Minh City, Vietnam（越南胡志明市国家大学科学大学）；National University of Singapore, Singapore（新加坡国立大学）；AISIA Lab, Ho Chi Minh City, Vietnam（越南胡志明市AISIA实验室）
PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（虚拟现实技术与系统国家重点实验室，北京航空航天大学）；Department of Computer Science, Durham University（杜伦大学计算机科学系）；Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University（人工智能研究院，北京人工智能研究院，清华大学-博世联合机器学习中心，THBI实验室，清华大学计算机科学与技术系）；Zhongguancun Laboratory, Beijing（中关村实验室，北京）
Enhancing Pneumonia Diagnosis and Severity Assessment through Deep Learning: A Comprehensive Approach Integrating CNN Classification and Infection Segmentation
Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery
Enhancing Image Generation Fidelity via Progressive Prompts
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Tsinghua University（清华大学）；Peking university（北京大学）；EaseUS（易我科技）；The Hong Kong University of Science and Technology（香港科技大学）
UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
School of Automation, Nanjing University of Science and Technology（自动化学院，南京理工大学）；National Key Laboratory of Information Systems Engineering（信息系统工程国家重点实验室）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）
Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）；Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）
Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible. Because such techniques are increasingly used in real-world applications, it is imperative to understand to what extent the privacy enhancement can be inverted and how much attribute information can be recovered from privacy-enhanced images. While these…
Friedrich-Alexander University Erlangen-Nuremberg（弗里德里希-亚历山大大学埃尔兰根-纽伦堡分校）；Erlangen Graduate School in Advanced Optical Technologies（埃尔兰根先进光学技术研究生院）；KU Leuven（鲁汶大学）；University Medical Center Mannheim, Medical Faculty Mannheim（曼海姆大学医学中心曼海姆医学院）
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biolo…
MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
GHIA Group, School of Engineering, Universidad Autónoma de Madrid, Spain（GHIA小组，工程学院，马德里自治大学，西班牙）；BiDA-Lab Group, School of Engineering, Universidad Autónoma de Madrid, Spain（BiDA实验室小组，工程学院，马德里自治大学，西班牙）
提出融合人工评估、多模态传感数据与 AI 分析的 MOSAIC-F 框架，通过四步流程为学生生成可执行的个性化演讲反馈。
AgentPose: Progressive Distribution Alignment via Feature Agent for Human Pose Distillation
Nanjing University of Posts and Telecommunications（南京邮电大学）；Surrey Institute for People-Centred Artificial Intelligence, University of Surrey（萨里大学以人为本人工智能研究所）
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through SVM regression. It offers higher correlation with subjective opinions compared to many conventional quality assessment methods. In this paper we propose enhancements to VMAF through the integration of new video features and alternative quality metrics (selected from a diverse pool) alongside multiple model combination. The proposed combination approach enables training on multiple databases with varying content and …
InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management
Nanjing University of Science and Technology（南京理工大学）；COMSATS University Islamabad - Sahiwal Campus（伊斯兰堡COMSATS大学萨希瓦尔校区）；University of Science and Technology of China（中国科学技术大学）
Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel "Progressive Patch Learning" approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. "Patch Learning" destructs the feature maps into patches and independently processes each local pat…
The Pennsylvania State University（宾夕法尼亚州立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shenzhen Baoan Women’s and Children’s Hospital, Jinan University（暨南大学深圳宝安妇幼保健院）；Xi’an University of Architecture and Technology（西安建筑科技大学）
General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA, and more importantly, augmenting raw video with infant body parsing and pose estimation information can significantly improve performance. To solve the problem of efficiently utilizing partially labeled IMVs for body parsing, we propose a semi-supervised model, termed SiamParseNet (SPN), which consists of…
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Shanghai Grandhonor Information Technology Co.Ltd（上海冠勇信息科技有限公司）；New York University（纽约大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）
This paper studies the problem of Person Re-Identification (ReID)for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory consumption, inhibiting its practicability in large-scale applications. This paper aims to develop a novel learning strategy to find efficient feature embeddings while maintaining the balance of accuracy and model complexity. More specifically, we find by enhancing the classical triplet loss together with cross-entropy loss, our method can explore the hard exa…
