activity
20202026
most citedSource-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

9 citations · 15 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results

Xin Li, Daoli Xu, Wei Luo +27

This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how to evaluate the loss of sem…

cs.CV2026

ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking

Kanglong Fan, Tianhe Wu, Wen Wen +6

Reasoning-induced vision-language models (VLMs) advance image quality assessment (IQA) with textual reasoning, yet their scalar scores often lack sensitivity and collapse to a few…

cs.CV2022★ 9 cited

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

Jianzhao Liu, Xin Li, Shukun An +1

Existing learning-based methods for blind image quality assessment (BIQA) are heavily dependent on large amounts of annotated training data, and usually suffer from a severe perfor…

cs.CV2022★ 2 cited

StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment

Yiting Lu, Xin Li, Jianzhao Liu +1

Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming,…

cs.CV2022

SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

Jianzhao Liu, Xin Li, Yanding Peng +2

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment…

cs.MM2021★ 1 cited

LIQA: Lifelong Blind Image Quality Assessment

Jianzhao Liu, Wei Zhou, Jiahua Xu +3

Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deploy…

eess.IV2020★ 3 cited

FAN: Frequency Aggregation Network for Real Image Super-resolution

Yingxue Pang, Xin Li, Xin Jin +4

Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achiev…

eess.IV2020

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Jianzhao Liu, Jianxin Lin, Xin Li +3

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.…