948 citations · 3k across the 36 of their papers we have counts for
24 papers · 1 filter
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators
Ce Chen, Congrui Wang, Yonglin Li +23
Text-to-image (T2I) diffusion models typically require substantial computational resources and cloud infrastructure, posing significant challenges for edge deployment in terms of l…
PrISM-IQA: Image Quality Assessment Made Practical for Smartphone Photography
Shuyan Zhai, Jiaqi He, Weixia Zhang +4
Existing smartphone image quality assessment (IQA) methods commonly reduce perceptual quality to a single score. However, this scalar formulation is poorly aligned with practical i…
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
Wei Sun, Wen Wen, Xiongkuo Min +3
Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applicatio…
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization
Mu Li, Kanglong Fan, Kede Ma
Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and…
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers
Ronghuan Wu, Wanchao Su, Kede Ma +1
Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG…
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Wei Shang, Dongwei Ren, Yi Yang +3
Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and le…
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Weixia Zhang, Guangtao Zhai, Ying Wei +2
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automate…
Learning a Deep Color Difference Metric for Photographic Images
Haoyu Chen, Zhihua Wang, Yang Yang +2
Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photog…
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
Weixia Zhang, Dingquan Li, Xiongkuo Min +4
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA mode…
Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches
Peibei Cao, Dingquan Li, Kede Ma
Learning-based image quality assessment (IQA) has made remarkable progress in the past decade, but nearly all consider the two key components -- model and data -- in isolation. Spe…
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Wen Wen, Mu Li, Yiru Yao +5
Investigating how people perceive virtual reality (VR) videos in the wild (i.e., those captured by everyday users) is a crucial and challenging task in VR-related applications due…
Measuring Perceptual Color Differences of Smartphone Photographs
Zhihua Wang, Keshuo Xu, Yang Yang +5
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophys…
Image Quality Assessment in the Modern Age
Kede Ma, Yuming Fang
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA). From an actionable perspective, we will first…
Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping
Chenyang Le, Jiebin Yan, Yuming Fang +1
We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normali…
Task-Specific Normalization for Continual Learning of Blind Image Quality Models
Weixia Zhang, Kede Ma, Guangtao Zhai +1
In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability…
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Zhihua Wang, Dingquan Li, Kede Ma
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse." Here we investigate a semi-supervised ensem…
Troubleshooting Blind Image Quality Models in the Wild
Zhihua Wang, Haotao Wang, Tianlong Chen +2
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When…
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
Jiebin Yan, Yu Zhong, Yuming Fang +2
Semantic segmentation is an extensively studied task in computer vision, with numerous methods proposed every year. Thanks to the advent of deep learning in semantic segmentation,…
Continual Learning for Blind Image Quality Assessment
Weixia Zhang, Dingquan Li, Chao Ma +3
The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel di…
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
Weixia Zhang, Kede Ma, Guangtao Zhai +1
Performance of blind image quality assessment (BIQA) models has been significantly boosted by end-to-end optimization of feature engineering and quality regression. Nevertheless, d…
Image Quality Assessment: Unifying Structure and Texture Similarity
Keyan Ding, Kede Ma, Shiqi Wang +1
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original. Relative to human observers, these measures are overly sen…
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Weixia Zhang, Kede Ma, Guangtao Zhai +1
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically disto…
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Kede Ma, Wentao Liu, Tongliang Liu +2
Objective assessment of image quality is fundamentally important in many image processing tasks. In this work, we focus on learning blind image quality assessment (BIQA) models whi…
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
Kede Ma, Huan Fu, Tongliang Liu +2
The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood. Traditional views of blur such as reduction in energy…