  This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge,
focused on User-Generated Content (UGC). The aim of this challenge is to gather
deep learning-based methods capable of estimating the perceptual quality of UGC
videos. The user-generated videos from the YouTube UGC Dataset include diverse
content (sports, games, lyrics, anime, etc.), quality and resolutions. The
proposed methods must process 30 FHD frames under 1 second. In the challenge, a
total of 102 participants registered, and 15 submitted code and models. The
performance of the top-5 submissions is reviewed and provided here as a survey
of diverse deep models for efficient video quality assessment of user-generated
content.
