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Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos

Mitra, S and Soundararajan, R (2024) Knowledge Guided Semi-supervised Learning for Quality Assessment of User Generated Videos. In: UNSPECIFIED, pp. 4251-4260.

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Official URL: https://doi.org/10.1609/aaai.v38i5.28221

Abstract

Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a selfsupervised Spatio-Temporal Visual Quality Representation Learning (ST-VQRL) framework to generate robust quality aware features for videos. Then, we propose a dual-model based Semi Supervised Learning (SSL) method specifically designed for the Video Quality Assessment (SSL-VQA) task, through a novel knowledge transfer of quality predictions between the two models. Our SSL-VQA method uses the ST-VQRL backbone to produce robust performances across various VQA datasets including cross-database settings, despite being learned with limited human annotated videos. Our model improves the state-of-the-art performance when trained only with limited data by around 10, and by around 15 when unlabelled data is also used in SSL. Copyright © 2024, Association for the Advancement of Artificial Intelligence.

Item Type: Conference Paper
Publication: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence
Additional Information: The copyright for this article belongs to authors.
Keywords: Supervised learning, Dual model; Large-scales; Learning frameworks; Perceptual quality; Quality assessment; Semi-supervised learning; Spatio-temporal; User-generated; User-generated video; Visual qualities, Knowledge management
Department/Centre: Others
Date Deposited: 21 May 2024 05:22
Last Modified: 21 May 2024 05:22
URI: https://eprints.iisc.ac.in/id/eprint/84815

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