Effects of Social Media Use on Brain Dynamics in Young Male Adults: Multistage Concurrent Electroencephalography-Functional Magnetic Resonance Imaging Study.

Underwater salient object detection aims to identify the most conspicuous objects or regions in underwater images [–]. Underwater salient object detection faces unique challenges, including light attenuation, color distortion, water scattering, and the blurring of object shapes and textures []. Meanwhile, the reliance on data from underwater sensors, such as RGB cameras and depth sensors, introduces complexities related to sensor noise and modality misalignment. Effectively addressing these challenges requires robust fusion techniques to integrate information from multi-sensor. With the advancement of deep learning techniques, RGB-based underwater object detection has achieved improvements in both accuracy and speed [–]. With the advancement of data-driven approaches, the construction of the USOD10K dataset —the first large-scale underwater RGB-D benchmark—has provided a solid foundation for training robust models across diverse and complex scenarios. Building upon this, researchers have explored more sophisticated fusion architectures. For instance, Wang et al. proposed [], which utilizes a hierarchical cross-modality attention network to effectively suppress underwater noise and generate more accurate saliency maps by refining multi-modal interactions. To address the issue of poor visibility, [] introduced the FocusAugment model, which leverages blurriness guidance to differentiate between multi-focus and low-focus regions, thereby enhancing the diversity of training samples. Furthermore, late-breaking studies have shifted towards addressing extreme underwater degradation. Recent models such as [] employ a cross-scale interaction strategy to maintain boundary integrity under severe light attenuation. The method proposed in [] optimizes multimodal feature interaction and enhancement through a two-stage training strategy. It further introduces a cross-scale learning strategy to promote coarse-to-fine feature fusion, thereby alleviating indistinct object boundaries in underwater vision tasks. By incorporating these hierarchical and attention-based strategies, contemporary methods have significantly reduced the uncertainty inherent in underwater multi-sensor data. However, optical distortions, water patterns, and the lack of geometric information limit the practical applications of RGB-based methods [,]. Incorporating depth information into the underwater object detection process has proven to be an effective solution [–]. Depth images, compared to RGB images, provide valuable information about object shapes, depths, and other geometric features [–]. These geometric features are particularly useful in underwater scenarios, as they help address occlusion issues and low visibility. By combining complementary information from depth and RGB images, more comprehensive underwater object detection can be achieved [–]. Despite its advantages, fully leveraging the complementary nature of RGB and depth images remains a challenging task due to their inherent differences [–]. Additionally, depth images may contain substantial noise due to the limited detection range and imaging conditions of depth cameras. [–] Traditional methods primarily focus on effectively fusing multimodal information using dual-branch architectures to extract RGB and depth image features separately [–]. While these approaches provide solid solutions for multimodal information fusion, they struggle to ensure the effective alignment of RGB and depth information [–], especially in complex underwater environments. Attempts to reduce sensitivity to depth information have been made, but implicit interactions between RGB features and depth information often lead to reduced model efficiency [–]. To summarize, contemporary underwater RGB-D fusion frameworks are hindered by three major challenges. First, underwater depth maps often suffer from quality degradation caused by backscattering and sensor limitations, reducing the reliability of cross-modal guidance. Second, significant modality discrepancies between RGB and depth images introduce severe spatial and channel-wise alignment ambiguity, limiting effective information interaction. Third, existing attention mechanisms generally compress either spatial or channel dimensions, leading to cross-dimensional context loss and insufficient modeling of dense global correlations required for accurate boundary perception.