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The aim is to improve underwater image quality assessment by developing a domain-robust evaluation framework using semantic contrastive learning.

Semantic Contrast for Domain-Robust Underwater Image Quality Assessment

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The aim is to improve underwater image quality assessment by developing a domain-robust evaluation framework using semantic contrastive learning.
Proposed SCUIA framework for unsupervised underwater image quality assessment.
Utilized vision-language contrastive learning to align image features with textual embeddings.
Introduced hierarchical contrastive learning with image-specific statistical priors and semantic prompts.
Implemented a triplet-based inter-group contrastive loss to model relative quality relationships.
Developed an unsupervised domain adaptation module for guiding fine-tuning of domain-invariant representations.
Significant improvements in cross-domain quality prediction compared to existing methods.
Demonstrated superior generalization across aquatic environments with zero-shot capabilities.
Achieved better discrimination of image quality under diverse degradation conditions.

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Unsupervised framework enhances quality prediction in underwater images, suggesting robust assessment across environments.

Zhou et al. (2026) studied this question.

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