50 citations · 131 across the 20 of their papers we have counts for
20 papers
MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce
Hao Jiang, Haoxiang Zhang, Qingshan Hou +4
Providing high-quality item recall for text queries is crucial in large-scale e-commerce search systems. Current Embedding-based Retrieval Systems (ERS) embed queries and items int…
Q-Ground: Image Quality Grounding with Large Multi-modality Models
Chaofeng Chen, Sensen Yang, Haoning Wu +6
Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual conten…
G-Refine: A General Quality Refiner for Text-to-Image Generation
Chunyi Li, Haoning Wu, Hongkun Hao +7
With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perce…
LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM
Zicheng Zhang, Haoning Wu, Yingjie Zhou +7
Although large multi-modality models (LMMs) have seen extensive exploration and application in various quality assessment studies, their integration into Point Cloud Quality Assess…
Towards Open-ended Visual Quality Comparison
Haoning Wu, Hanwei Zhu, Zicheng Zhang +11
Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardi…
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Haoning Wu, Zicheng Zhang, Weixia Zhang +11
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. W…
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
Chaofeng Chen, Shangchen Zhou, Liang Liao +4
Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative…
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Haoning Wu, Zicheng Zhang, Erli Zhang +11
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range o…
Enhancing Diffusion Models with Text-Encoder Reinforcement Learning
Chaofeng Chen, Annan Wang, Haoning Wu +4
Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such…
Local Distortion Aware Efficient Transformer Adaptation for Image Quality Assessment
Kangmin Xu, Liang Liao, Jing Xiao +4
Image Quality Assessment (IQA) constitutes a fundamental task within the field of computer vision, yet it remains an unresolved challenge, owing to the intricate distortion conditi…
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
Chaofeng Chen, Jiadi Mo, Jingwen Hou +5
Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the hum…
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
Xiaohong Liu, Xiongkuo Min, Wei Sun +69
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement W…
Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
Haoning Wu, Erli Zhang, Liang Liao +6
The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQ…
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment
Haoning Wu, Liang Liao, Annan Wang +5
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies. Contemporary supervi…
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Haoning Wu, Liang Liao, Jingwen Hou +6
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across…
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
Haoning Wu, Erli Zhang, Liang Liao +6
The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA pr…
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
Haoning Wu, Chaofeng Chen, Liang Liao +5
The increased resolution of real-world videos presents a dilemma between efficiency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original reso…
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
Liang Liao, Kangmin Xu, Haoning Wu +4
With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts…
Deep Face Video Inpainting via UV Mapping
Wenqi Yang, Zhenfang Chen, Chaofeng Chen +2
This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of an…
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Guanying Chen, Chaofeng Chen, Shi Guo +3
High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR)…