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Perceptual quality assessment of user generated content (UGC) videos ischallenging due to the requirement of large scale human annotated videos fortraining. In this work, we address this challenge by first designing aself-supervised 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) methodspecifically designed for the Video Quality Assessment (SSL-VQA) task, througha novel knowledge transfer of quality predictions between the two models. OurSSL-VQA method uses the ST-VQRL backbone to produce robust performances acrossvarious VQA datasets including cross-database settings, despite being learnedwith limited human annotated videos. Our model improves the state-of-the-artperformance when trained only with limited data by around 10%, and by around15% when unlabelled data is also used in SSL. Source codes and checkpoints areavailable at https://github.com/Shankhanil006/SSL-VQA.

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