Current deep video quality assessment (VQA) methods are usually with high computational
costs when evaluating high-resolution videos. This cost hinders them from learning better …

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 …

User-generated content (UGC) live videos are often bothered by various distortions during
capture procedures and thus exhibit diverse visual qualities. Such source videos are further …

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 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 …

Compared with spatial counterparts, temporal relationships between frames and their
influences on video quality assessment (VQA) are still relatively under-studied in existing …

Short-form UGC video platforms like Kwai and TikTok have been an emerging and
irreplaceable mainstream media form thriving on user-friendly engagement and …

Video captioning aims to generate natural language sentences that describe the given video
accurately. Existing methods obtain favorable generation by exploring richer visual …

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 …

Video quality assessment (VQA) has attracted growing attention in recent years. While the
great expense of annotating large-scale VQA datasets has become the main obstacle for …