54 citations · 363 across the 32 of their papers we have counts for
6 papers · 1 filter
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Haoning Wu, Zicheng Zhang, Erli Zhang +8
The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Neverthel…
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…
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…