Recent advances in few-shot novel-view synthesis based on 3D Gaussian Splatting (3DGS) have shown remarkable progress. Existing methods usually rely on carefully designed geometric regularizers to reinforce geometric supervision; however, applying multiple regularizers consistently across scenes is hard to tune and often degrades robustness. Consequently, generating reliable geometry from extremely sparse viewpoints remains a key challenge. To overcome this limitation, we introduce SREGS, a framework tailored for few-shot reconstruction whose contributions focus on two aspects: explicitly consistent geometry and multi-scale depth-guided optimization. Specifically, to explicitly optimize reconstruction consistency, we initialize the point cloud with 2D Gaussians, thereby enhancing depth consistency for the same Gaussian observed from different views. Secondly, we employ region-adaptive rapid densificationn to fill under-covered regions with additional representations, while an opacity-aware noise term injects stochasticity into each Gaussian to boost exploration in under-observed areas. In addition, to strengthen geometric refinement of the radiance field, we impose multi-scale depth constraints based on a monocular depth prior, performing geometric refinement from global to local scales and ensuring highly accurate reconstruction. Extensive experiments on LLFF, MipNeRF360, and Blender show that SREGS achieves higher synthesis quality with lower computational cost and demonstrates robust performance. The code is available at:https://github.com/LeeXiaoTong1/SREGS.

Multimodal Sentiment Analysis (MSA) is the technology of intelligently recognizing and assessing human sentiments using various data forms such as text, image, and audio. Despite current mainstream methods have made significant progress, MSA still faces the following issues: 1) most current methods train models based on pre-extracted features, lacking a sufficient understanding of sentiment diversity in multimodal data and may even lead to the loss of critical information in the raw data; and 2) textual modality, which possesses high-level semantic features, should typically dominate the fusion process, yet current methods fail to fully leverage this characteristic to balance modality information. To address the aforementioned issues, we propose a novel Multimodal Sentiment Analysis framework using Multimodal-Prefixed and Cross-Modal Attention (DB-MPCA). For the first issue, DB-MPCA employs multimodal raw data for pre-training, which not only allows for in-depth exploration of multimodal information but also significantly enhances the model’s learning capabilities and generalization, while reducing the substantial costs associated with manual annotation. Regarding the second issue, DB-MPCA introduces two prefix encoders designed to convert acoustic and visual features into prefix tokens. These tokens are then embedded into a pre-trained language model, where they are encoded together with textual tokens. Through this approach, DB-MPCA effectively learns cross-modal attention while maintaining the dominance of the textual modality, thereby optimizing the fusion of modalities. Comprehensive experiments conducted on the widely utilized dataset (CMU-MOSI) demonstrate the effectiveness of our model, highlighting its superiority over baseline models.