The field of underwater research is moving towards developing more robust and accurate methods for exploration and monitoring. This is driven by the need for efficient and cost-effective solutions for localization, mapping, and object detection in underwater environments. Recent developments have focused on improving the reliability of feature extraction and matching, as well as enhancing the quality of sonar and acoustic images. Notable papers in this area include: Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images, which proposes a novel adaptive GAN-synthesis method to generate environment-specific synthetic underwater images. Passive Underwater Acoustic Signal Separation based on Feature Decoupling Dual-path Network, which introduces a novel temporal network designed to separate ship radiated noise by employing a dual-path model and a feature decoupling approach. Real-time Seafloor Segmentation and Mapping, which combines machine learning and computer vision techniques to enable an autonomous underwater vehicle to inspect the boundaries of Posidonia oceanica meadows autonomously. Self-Supervised Enhancement of Forward-Looking Sonar Images: Bridging Cross-Modal Degradation Gaps through Feature Space Transformation and Multi-Frame Fusion, which proposes a feature-space transformation that maps sonar images from the pixel domain to a robust feature domain. UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques, which explores the use of underwater image enhancement techniques to improve keypoint detection and matching. Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data Gaps, which introduces a methodology that combines the high-fidelity 3D reconstruction capabilities of the SfM-MVS methods with state-of-the-art refraction correction techniques. Can Masked Autoencoders Also Listen to Birds?, which introduces Bird-MAE, a domain-specialized MAE pretrained on the large-scale BirdSet dataset. A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition, which proposes a multi-task balanced channel attention convolutional neural network to address the challenges of few-shot underwater acoustic target recognition.