Accurate measurement of image quality without reference signals remains a fundamental challenge in low-level visual perception applications. In this paper, we propose a global-local progressive integration model that addresses this challenge through three key contributions: 1) We develop a dual-measurement framework that combines vision Transformer (ViT)-based global feature extractor and convolutional neural networks (CNNs)-based local feature extractor to comprehensively capture and quantify image distortion characteristics at different granularities. 2) We propose a progressive feature integration scheme that utilizes multi-scale kernel configurations to align global and local features, and progressively aggregates them via an interactive stack of channel-wise self-attention and spatial interaction modules for multi-grained quality-aware representations. 3) We introduce a semantic-aligned quality transfer method that extends the training data by automatically labeling the quality scores of diverse image content with subjective opinion scores. Experimental results demonstrate that our model yields 5.04% and 5.40% improvements in Spearman's rank-order correlation coefficient (SROCC) for cross-authentic and cross-synthetic dataset generalization tests, respectively. Furthermore, the proposed semantic-aligned quality transfer further yields 2.26% and 13.23% performance gains in evaluations on single-synthetic and cross-synthetic datasets.
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…
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…
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…
University of Würzburg（维尔茨堡大学）；Institute of Computer Science（计算机科学研究所）
Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a quantitative metric to reflect the performance of new algorithms, which should be well-aligned with the person's mean opinion score (MOS). Learning-based approaches for perceptual image quality assessment (IQA) usua…
Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算与人工智能学院）；Harvard Medical School, Harvard University（哈佛大学医学院）
University of Illinois（伊利诺伊大学）；Huazhong University of Science and Technology（华中科技大学）；Microsoft（微软公司）
This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the condition and the target poses with attention mechanism. Two types of blocks are introduced, namely Pose-Attentional Transfer Block (PATB) and Aligned Pose-Attentional Transfer Bloc ~(APATB). Compared with previous works, our model generates more photorealistic person images that retain …
School of Computer Science and Technology（计算机科学与技术学院）；East China Normal University（东华师范大学）；School of Design（设计学院）
Artistic image assessment has become a prominent research area in computer vision. In recent years, the field has witnessed a proliferation of datasets and methods designed to evaluate the aesthetic quality of paintings. However, most existing approaches focus solely on static final images, overlooking the dynamic and multi-stage nature of the artistic painting process. To address this gap, we propose a novel framework for human-aligned assessment of painting processes. Specifically, we introduce the Painting Process Assessment Dataset (PPAD), the first large-scale dataset comprising real and …
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）
MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
Beihang University（北京航空航天大学）；Durham University（杜伦大学）；Tsinghua University（清华大学）；Children’s Hospital of Capital Institute of Pediatrics（首都儿科研究所附属儿童医院）；Zhongguancun Laboratory（中关村实验室）
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）
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）
MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving
Amap, Alibaba Group（阿里集团蚂巴公司）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong（香港中文大学）；Xi’an Jiaotong University（西安交通大学）
Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situation also applies to image quality assessment (IQA). Although recent no-reference (NR-IQA) methods have made great progress to predict image quality free from the reference image, they still have the potential to achieve better performance since HQ image information is not fully exploited. In contrast, full-reference (FR-IQA) methods tend to provide more reliable quality evaluation, but its practicability is affecte…
Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
University of Science and Technology of China（中国科学技术大学）；Harvard University（哈佛大学）；Department of CV, MBZUAI（MBZUAI计算机视觉部门）；Dalian University of Technology（大连理工大学）；Stanford University（斯坦福大学）；Chicago University（芝加哥大学）
TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
Lomonosov Moscow State University（洛蒙诺索夫莫斯科国立大学）；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与系统研究所在可信人工智能领域研究中心）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）
Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
Wuhan University（武汉大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Harbin Institute of Technology（哈尔滨工业大学）
UKRI CDT Biomedical AI（英国研究与创新署生物医学人工智能博士培养中心）；University of Edinburgh（爱丁堡大学）；School of Informatics（信息学院）；Usher Institute（厄舍研究所）
Deep learning (DL) models are very effective on many computer vision problems and increasingly used in critical applications. They are also inherently black box. A number of methods exist to generate image-wise explanations that allow practitioners to understand and verify model predictions for a given image. Beyond that, it would be desirable to validate that a DL model \textit{generally} works in a sensible way, i.e. consistent with domain knowledge and not relying on undesirable data artefacts. For this purpose, the model needs to be explained globally. In this work, we focus on image modal…
Britton Chance Center for Biomedical Photonics（布里顿·钱斯生物医学光子学研究中心）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；MoE Key Laboratory for Biomedical Photonics（教育部生物医学光子学重点实验室）；Collaborative Innovation Center for Biomedical Engineering（生物医学工程协同创新中心）；School of Engineering Sciences, Huazhong University of Science and Technology（华中科技大学工程科学学院）
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-…
Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Department of Psychological and Cognitive Sciences, Tsinghua University（心理与认知科学系，清华大学）；Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University（人工智能研究院、北京人工智能研究所、清华大学-博世联合机器学习中心、THBI实验室，清华大学）；Department of Computer Science, Durham University（计算机科学系，杜尔姆大学）；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）；Zhongguancun Laboratory, Beijing（中关村实验室，北京）
X-PCR: A Benchmark for Cross-modality Progressive Clinical Reasoning in Ophthalmic Diagnosis
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Tsinghua University（清华大学）；University of Nottingham（诺丁汉大学）；School of AI, Shenzhen University（深圳大学人工智能学院）；Wenzhou Medical University（温州医科大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）
TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Cairo University（开罗大学）
Video-Bench: Human-Aligned Video Generation Benchmark
Shanghai Jiao Tong University（上海交通大学）；Stanford University（斯坦福大学）；Fellou AI（Fellou人工智能）；Fudan University（复旦大学）；Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；Hong Kong Polytechnic University（香港理工大学）；University of Glasgow（格拉斯哥大学）；City University of Hong Kong（香港城市大学）；Westlake University（西湖大学）；LiveX AI（LiveX人工智能）；National University of Singapore（新加坡国立大学）
Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework
School of Automation, Southeast University（东南大学自动化学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Sports Training, Tianjin University of Sport（天津体育学院运动训练学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）
