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

Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman, Radu Timofte, Chenlong He +31 more
4/24/2024
cs.CVcs.MM

No official code found. Best implementation: mv-lab/VideoAI-Speedrun (5 stars, Apache-2.0, last commit 2024-04-03).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

mv-lab/VideoAI-SpeedrunCommunity100%
Apache-2.0

VQA & VSR Efficiency and Runtime Evaluation -- AIS 2024 Workshop @ CVPR

50PythonApr 1, 20242 years agoApache-2.0

Similar Papers

AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results

86% match1 repo252024

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

84% match2024

A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+

84% match2024

CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video

84% match1 repo132025

AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM

83% match2 repos192026

Modular Blind Video Quality Assessment

83% match1 repo362026

Cite this paper

@article{conde2024,
  title  = {AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results},
  author = {Marcos V. Conde and Saman Zadtootaghaj and Nabajeet Barman and Radu Timofte and Chenlong He and Qi Zheng and Ruoxi Zhu and Zhengzhong Tu and Haiqiang Wang and Xiangguang Chen and Wenhui Meng and Xiang Pan and Huiying Shi and Han Zhu and Xiaozhong Xu and Lei Sun and Zhenzhong Chen and Shan Liu and Zicheng Zhang and Haoning Wu and Yingjie Zhou and Chunyi Li and Xiaohong Liu and Weisi Lin and Guangtao Zhai and Wei Sun and Yuqin Cao and Yanwei Jiang and Jun Jia and Zhichao Zhang and Zijian Chen and Weixia Zhang and Xiongkuo Min and Steve Göring and Zihao Qi and Chen Feng},
  year   = {2024},
  eprint = {2404.16205},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.16205v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.16205.svg)](https://codeofpaper.com/paper/2404.16205)

Discussion