mlsanityData to 7 Oct 2026

AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

arXiv 2404.16205 ↗First posted 24 Apr 2024cs.CV cs.MMcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

23 of 46 references.

References · 23

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11 more
  1. 2018Large-Scale Study of Perceptual Video Qualitycited by 127
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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Modelscited by 28
  2. 2024Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Surveycited by 19
  3. 2024Video Quality Assessment: A Comprehensive Surveycited by 15
  4. 2025Prompt-aware of Frame Sampling for Efficient Text-Video Retrievalcited by 0