The field of underwater image and video enhancement is rapidly advancing, with a focus on developing innovative methods to improve the quality and accuracy of underwater visual data. Recent developments have highlighted the importance of incorporating human perception and subjective image quality into the enhancement process, as well as the need for more robust and generalizable approaches to handling challenging underwater environments. Notable advancements include the use of deep learning-based methods, such as generative adversarial networks and contrastive language-image pre-training, to enhance underwater image quality and perception. Additionally, there is a growing interest in developing specialized tracking frameworks and benchmarks for underwater multiple fish tracking, which has important applications in marine ecology and aquaculture. Overall, the field is moving towards more sophisticated and nuanced approaches to underwater image and video enhancement, with a focus on improving the accuracy, robustness, and generalizability of these methods. Noteworthy papers include: Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration, which proposes a deep learning-based approach to integrating human subjective assessments into the training process. Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement, which introduces a UIE method with a Contrastive Language-Image Pre-Training perception loss module and curriculum contrastive regularization. When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking, which presents a comprehensive dataset and tracking framework for underwater multiple fish tracking.