Underwater image quality assessment (UIQA) is hindered by complex degradation and domain shifts across aquatic environments. Existing no-reference IQA methods rely on costly and subjective mean opinion scores (MOS), which limit their generalization to unseen domains. To overcome these challenges, we propose SCUIA, an unsupervised UIQA framework leveraging semantic contrastive learning for quality prediction without human annotations. Specifically, we introduce a vision-language contrastive learning strategy that aligns image features with textual embeddings in a unified semantic space, capturing implicit degradation-quality correlations. We further enhance quality discrimination with a hierarchical contrastive learning mechanism that combines image-specific statistical priors and semantic prompts. A triplet-based inter-group contrastive loss explicitly models relative quality relationships. To tackle cross-domain variations, we develop an unsupervised domain adaptation module that uses local statistical features to guide CLIP fine-tuning to disentangle domain-invariant quality representations from domain-specific noise. This enables zero-shot cross-domain quality prediction without labeled data. Extensive experiments on public UIQA benchmarks demonstrate significant improvements over existing methods, highlighting superior generalization and domain adaptability.

• Propose a multimodal-inspired underwater video enhancement framework. • Introduce collision-aware noise injection to simulate feature-level conflicts. • Design a hybrid ViT-convolutional architecture with low computational cost. • Achieve improved clarity and temporal consistency across underwater frames. Underwater video enhancement addresses degradation from absorption, scattering, and turbidity, which hinders visual tasks such as detection and tracking. Unlike traditional single-frame methods, we treat temporal cues, occlusion patterns, and structured degradations as implicit modalities within video streams. To this end, we propose CAECNet (Collision-Attack Error Correction Network), a novel multimodal-inspired underwater video enhancement network that integrates the ‘Collision Attack’ training strategy with the ‘Error Correction’ mechanism. This network overcomes the limitations of traditional single-frame enhancement methods, implementing high-precision and real-time inference. It improves temporal perception through multi-frame fusion and utilizes previous frames to assist real-time inference, meeting the demands of dynamic processing. By incorporating a Vision Transformer (ViT) and a lightweight depthwise separable convolution module, the network enhances spatial feature representation and computational efficiency. A branching-based error correction upsampler is designed to correct feature representation errors and reduce information entropy loss, thereby improving video detail restoration quality. The “Collision Attack” training strategy injects structured noise to accelerate network feature learning and reduce computational costs. Experimental results show that CAECNet significantly outperforms existing methods on multiple underwater video datasets, improving image clarity, inter-frame consistency, and computational efficiency, making it suitable for underwater robotic intelligent perception tasks.

Underwater robots rely on high-quality visual data for precise monitoring and manipulation, yet complex underwater environments often degrade image quality through color distortion, texture blurring, and detail loss. Existing enhancement methods partially address these issues, but fail to effectively decouple nonlinear relationships among degradation factors, leading to inconsistent performance. To address these challenges, we propose a degradation-content decoupling-based underwater image enhancement network (DCDN). The framework integrates a super-fusion cascade module for dynamic feature weighting, reducing artifacts, and combines multichannel color space transformation with texture-guided correction to decouple and optimize degradation factors. This approach improves color fidelity and texture detail restoration by refining color information and adapting local textures. Experiments on public datasets demonstrate that DCDN outperforms existing methods in various underwater scenarios. This work enhances the visual capabilities of underwater robots, supporting intelligent transportation applications, such as marine logistics and underwater inspections.

Relation prediction in knowledge graphs is a classic task. However, predicting unseen facts under the open-world assumption remains challenging, as conventional closure reasoning methods tend to fail. To address this, we propose an uncertain knowledge graph embedding and Bayesian inferring model, UKGEBN, which examines the capabilities of approximate inference under the open-world assumption. This model employs a large language model to encode entities and relations by treating facts as natural language sentences. It then learns knowledge confidence through a recurrent neural network and subsequently constructs Bayesian networks for the uncertain knowledge graph based on the learned confidence. The constructed Bayesian network can reasonably infer unseen knowledge facts, even if the elements of these facts-such as new entities and relationships-have never been encountered in the knowledge base before. The experimental results show that the new model has improved the MSE metric by 24.6% in relation prediction on the benchmark dataset. Case studies indicate that the model significantly enhances the revelation of latent semantic information, facilitating the prediction of implicit relationships in uncertain knowledge graphs.

Underwater image quality assessment (UIQA) is a challenging task due to the complexities of underwater environments. Traditional UIQA methods primarily rely on fitting mean opinion scores (MOS), which are limited by human visual biases. To address the above limitation, we propose a no-reference underwater image quality assessment paradigm using reinforcement sequences. Our paradigm leverages reinforcement learning to iteratively merge the input image with the corresponding ground truth, generating an optimized sequence of images. A classifier generates probability arrays for the optimized sequence, which are converted into objective scores by a regression model. Unlike existing methods that focus solely on the final quality score, our paradigm emphasizes dynamic quality changes throughout the image-enhancement process. By employing objective mixing ratio labels, our reinforcement sequence dataset reduces subjective bias. The multiscale classifier captures local and global information differences between the input and ground truth images, effectively preserving the contrast and detail in diverse lighting conditions. Our paradigm combines multi-source data classification with support vector regression, optimizing the mapping of feature vectors to quality scores through fine-tuning libsvm kernel parameters. Experimental results on multiple benchmark datasets demonstrate that our paradigm outperforms the state-of-the-art UIQA methods, providing an effective solution for Underwater Image quality Assessment via Reinforcement Sequences (RSUIA).