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Found 5 papers, 4 papers with code
Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training.
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications.
Test Time Adaptation for Blind Image Quality Assessment
In this work, we introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA.
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human annotations of quality.
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content
Completely blind video quality assessment (VQA) refers to a class of quality assessment methods that do not use any reference videos, human opinion scores or training videos from the target database to learn a quality model.
