9 citations · 16 across the 18 of their papers we have counts for
18 papers
LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)
Wei Luo, Yiting Lu, Xin Li +32
This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings. The…
DiffCap-Bench: A Comprehensive, Challenging, Robust Benchmark for Image Difference Captioning
Yuancheng Wei, Haojie Zhang, Linli Yao +7
Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key benchmark for fine-grained change…
OmniQuality-R: Advancing Reward Models Through All-Encompassing Quality Assessment
Yiting Lu, Fengbin Guan, Yixin Gao +8
Current visual evaluation approaches are typically constrained to a single task. To address this, we propose OmniQuality-R, a unified reward modeling framework that transforms mult…
NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
Xiaohong Liu, Xiongkuo Min, Qiang Hu +92
This paper reports on the NTIRE 2025 XGC Quality Assessment Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) a…
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study
Xin Li, Xijun Wang, Bingchen Li +7
In this work, we build the first benchmark dataset for short-form UGC Image Super-resolution in the wild, termed KwaiSR, intending to advance the research on developing image super…
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
Xin Li, Kun Yuan, Bingchen Li +110
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Qualit…
Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
Yiting Lu, Xin Li, Haoning Wu +3
The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two p…
InternVQA: Advancing Compressed Video Quality Assessment with Distilling Large Foundation Model
Fengbin Guan, Zihao Yu, Yiting Lu +2
Video quality assessment tasks rely heavily on the rich features required for video understanding, such as semantic information, texture, and temporal motion. The existing video fo…
Priorformer: A UGC-VQA Method with content and distortion priors
Yajing Pei, Shiyu Huang, Yiting Lu +2
User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models f…
QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Fengbin Guan, Xin Li, Zihao Yu +2
In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and exca…
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li, Kun Yuan, Yajing Pei +65
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected da…
KVQ: Kwai Video Quality Assessment for Short-form Videos
Yiting Lu, Xin Li, Yajing Pei +6
Short-form UGC video platforms, like Kwai and TikTok, have been an emerging and irreplaceable mainstream media form, thriving on user-friendly engagement, and kaleidoscope creation…
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
Zihao Yu, Fengbin Guan, Yiting Lu +2
The objective of non-reference video quality assessment is to evaluate the quality of distorted video without access to reference high-definition references. In this study, we intr…
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…
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,…
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition
Zongze Chen, Wenxia Yang, Xin Li
Chinese characters carry a wealth of morphological and semantic information; therefore, the semantic enhancement of the morphology of Chinese characters has drawn significant atten…
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Yiting Lu, Jun Fu, Xin Li +6
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appr…
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…