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Found 18 papers, 14 papers with code
NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes.
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024.
Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency
To address this problem, we design a multi-branch deep neural network (DNN) to assess the quality of UHD images from three perspectives: global aesthetic characteristics, local technical distortions, and salient content perception.
Dual-Branch Network for Portrait Image Quality Assessment
We employ the fidelity loss to train the model via a learning-to-rank manner to mitigate inconsistencies in quality scores in the portrait image quality assessment dataset PIQ.
Ranked #1 on
Face Image Quality Assessment
on PIQ23
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media.
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
In this work, we show -- for the first time -- that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos
However, performance evaluation research lags behind the development of talking head generation techniques.
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents.
Ranked #1 on
Video Quality Assessment
on LIVE-FB LSVQ
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information.
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task.
Ranked #4 on
Video Quality Assessment
on MSU NR VQA Database
Task-Specific Normalization for Continual Learning of Blind Image Quality Models
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 trade-off, and task-order/-length robustness.
Continual Learning for Blind Image Quality Assessment
In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.
Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning
We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference.
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa).
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Ranked #2 on
Video Quality Assessment
on MSU NR VQA Database
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.
