The field of underwater vision and acoustics is witnessing significant advancements, particularly in enhancing robustness and adaptability to complex environmental conditions. Innovations in underwater instance segmentation are focusing on adaptive channel attention mechanisms to dynamically adjust feature weights, thereby improving segmentation performance in challenging scenarios such as light attenuation and color distortion. In underwater acoustic target recognition, there is a shift towards multi-task learning frameworks that incorporate auxiliary tasks to capture robust patterns of targets, enhancing generalization and stability. Additionally, adversarial learning mechanisms are being integrated to improve robustness against various influential factors. These developments collectively aim to advance the reliability and accuracy of underwater sensing technologies, making them more applicable in real-world scenarios.

Noteworthy papers include one proposing an adaptive channel attention mechanism for underwater instance segmentation, significantly improving performance in high-precision tasks, and another introducing a multi-task learning framework with adversarial mechanisms, achieving state-of-the-art performance in underwater acoustic target recognition.