Boosting Image Quality Assessment with PromptIQA
PromptIQA introduces a novel approach to adapt to new assessment requirements without fine-tuning, using prompts for targeted predictions. The model outperforms existing methods by learning diverse requirements and achieving better generalization.
PromptIQA: Boosting Image Quality Assessment via Prompts
PromptIQA can adapt to new requirements without fine-tuning, outperforming existing methods.
Attention Down-Sampling Transformer, Relative Ranking, and Self-Consistency for Blind Image Quality Assessment
An improved mechanism to extract local and non-local information from images via different transformer encoders and CNNs, establishing a stronger connection between subjective and objective assessments through sorting within batches of images based on relative distance information, and a self-consistency approach to self-supervision to address the degradation of no-reference image quality assessment models under equivariant transformations.
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