Video generation has witnessed significant advancements yet evaluating these models
remains a challenge. A comprehensive evaluation benchmark for video generation is …

Multi-modality large language models (MLLMs) as represented by GPT-4V have introduced
a paradigm shift for visual perception and understanding tasks that a variety of abilities can …

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 …

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 …

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, VQA on …

The explosion of visual content available online underscores the requirement for an
accurate machine assessor to robustly evaluate scores across diverse types of visual …

Video quality assessment especially for a massive scale of user-generated content is an
essential yet challenging computer vision and video analysis problem. Prior methods have …

Quality assessment, which evaluates the visual quality level of multimedia experiences, has
garnered significant attention from researchers and has evolved substantially through …

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and
improving the end-users' viewing experience in various real-world video-enabled media …

Recently, Users Generated Content (UGC) videos becomes ubiquitous in our daily lives.
However, due to the limitations of photographic equipments and techniques, UGC videos …