The field of marine computer vision is moving towards more efficient and accurate methods for underwater image enhancement, object detection, and behavioral monitoring. Researchers are exploring the use of deep learning techniques, such as transformer-based neural networks and CLIP embeddings, to improve the robustness and accuracy of these methods. One notable trend is the development of large-scale benchmarks, such as FishDet-M, which provides a unified platform for evaluating object detection models in diverse aquatic environments. Another area of focus is the application of computer vision to real-time monitoring and decision-making, including the use of zero-shot model selection frameworks to dynamically identify the most suitable detector for a given input image. Noteworthy papers include:

• EBA-AI, which introduces an ethics-guided bias-aware AI framework for efficient underwater image enhancement and coral reef monitoring,
• FishDet-M, which establishes a standardized and reproducible platform for evaluating object detection in complex aquatic scenes.