The field of underwater scene understanding is moving towards improved performance in challenging environments, with a focus on domain adaptation and robustness to varying conditions. Researchers are exploring innovative approaches to address the unique challenges posed by underwater scenes, such as light attenuation, color distortion, and turbidity. Notable advancements include the development of fine-tuned models that can effectively estimate metric depth in underwater scenes and enhance image quality. These advancements have the potential to significantly improve underwater exploration and observation capabilities. Noteworthy papers include:

• A study on monocular metric depth estimation that demonstrates the importance of domain adaptation for achieving robust performance in underwater environments.
• The introduction of the MAC-Lookup model for underwater image enhancement, which showcases impressive results in restoring details and colors in underwater images.
• The creation of the UVLM benchmark for underwater world understanding, which provides a comprehensive evaluation framework for video language models in underwater scenarios.