Dynamic underwater novel-view synthesis remains challenging because refraction, scattering, and particle interference undermine correspondence reliability, geometric consistency, and appearance stability. We propose SeaAnchor-GS, a robust dynamic 3D Gaussian splatting framework for underwater scene reconstruction. Each Gaussian is associated with a persistent identity embedding and deformed via identity–time conditioning, which improves deformation estimation under unstable underwater observations. A dual-branch residual dynamics module captures both dominant motion and fine-scale variations, while confidence-guided sampling, progressive deformation activation, and neighborhood-consistency regularization enhance optimization robustness and model compactness. Extensive experiments on dynamic underwater benchmarks show consistent improvements in reconstruction fidelity and perceptual quality, with a favorable balance between quality, efficiency, and representation compactness. Additional results on a static underwater benchmark suggest that the proposed representation remains competitive beyond the dynamic setting.

Exploring complementary information between RGB and thermal/depth modalities is crucial for bi-modal salient object detection (BSOD). However, the distinct characteristics of different modalities often lead to large differences in information distributions. Existing models, which rely on convolutional operations or plug-and-play attention mechanisms, struggle to address this issue. To overcome this challenge, we rethink the relationship between information complementarity and long-range relevance, and propose a uniform broad-view Twins Transformer Network (TwinsTNet) for accurate BSOD. Specifically, to efficiently fuse bi-modal information, we first design the Cross-Modal Federated Attention (CMFA), which mines complementary cues across modalities through element-wise global dependency. Second, to ensure accurate modality fusion, we propose the Semantic Consistency Attention Loss, which supervises the co-attention feature in CMFA using the ground-truth-generated attention map. Additionally, existing BSOD models lack the exploration of inter-layer interactions, for which we propose the Cross-Scale Retracing Attention (CSRA), which retrieves query-relevant information from stacked features of all previous layers, enabling flexible cross-layer interactions. The cooperation between CMFA and CSRA mitigates inductive bias in both modality and layer dimensions, enhancing TwinsTNet's representational capability. Extensive experiments demonstrate that TwinsTNet outperforms twenty-two existing state-of-the-art models on ten BSOD benchmark datasets. The code is available at: https://github.com/JoshuaLPF/TwinsTNet.

Numerous bottom-up salient object detection algorithms formulate the problem as a classification task. For an input image, these methods usually utilize prior cues to select some regions as training set, and learn a classifier to classify all regions into foreground/background. However, such binary classification based approaches suffer from accuracy problems in some complex scenes. To this end, we propose a novel framework, namely Multi-Subclass Classification with Label Distribution Learning (MSCLDL). Specifically, prior knowledge is firstly employed to build a training set from input image, in which each sample is associated with one of two class labels. Previous works usually learn directly a binary classification model from training set. Different with them, we further decompose two classes into a certain number of subclasses, each sample is thus described by one of multiple subclass labels. Based on the multi-subclass training set, we learn a label distribution model to predict the subclass label of each image region. Furthermore, the saliency value of each image region could be computed via exploring the relationship class and subclass labels. The MSCLDL could overcome the limitation of existing classification-based algorithms in some challenging scenes. Finally, a novel refinement technology is presented to further refine the saliency map obtained by MSCLDL. We compare the proposed method and other state-of-the-art methods on four benchmark datasets, the superiority of our model is adequately demonstrated via the experimental results analysis.