The field of computer vision is moving towards more accurate and robust boundary-aware semantic segmentation, with a focus on improving performance in challenging environments such as underwater imaging. Recent advancements have led to the development of novel frameworks and techniques that enhance the accuracy and reliability of semantic segmentation models. Notable progress has been made in addressing the issues of blurred target boundaries, insufficient recognition of small targets, and degraded image quality in underwater environments. The integration of boundary enhancement features, multi-scale aggregation attention, and frequency-guided fusion modules has shown significant improvements in segmentation performance. Furthermore, the development of large-scale datasets and benchmarking protocols has facilitated the evaluation and comparison of different models, driving innovation and advancements in the field.

Some noteworthy papers include: The paper proposing a Hybrid Multi-Stage Learning Framework for edge detection, which achieves state-of-the-art performance on benchmark datasets. The work on BoundMatch, a novel multi-task semi-supervised segmentation framework that explicitly integrates semantic boundary detection into the consistency regularization pipeline, achieving competitive performance against state-of-the-art methods. The introduction of the Marine Debris Forward-Looking Sonar datasets, which provides a valuable resource for the research community to develop and evaluate sonar-based object classification, detection, and segmentation models.