A cross-stage features fusion network for building extraction from remote sensing imagesView further author informationhttps://orcid.org/0000-0003-3436-4251Yu WangView further author informationhttps://orcid.org/0000-0002-4323-382XXiao Huang
However, these two-stage methods require a large amount of time to determine the candidate regions, which results in low real-time performance. Moreover, there is greater overlap between candidate boxes, which leads to redundant feature extraction operations and occupies too much storage space. One...
(CRASM) and Adapt Feature Fusion Module (AFFM). Specifically, first, we designed a lightweight model named LHRNet. This model is obtained by replacing the second-stage and fourth-stage Basic blocks of HRNet with the DMSC-Basic block designed by us. The DMSC-Basic block is composed of ...
The pipeline of the cross-modal fusion. The is a language feature map of same spatial sizes as the updated visual feature . and together form a Image-Language graph, and the graph attention network is used for cross-modal fusion. The important structure in the first stage is scaled dot-...
blur kernels of left and right parallax images, establishing a connection between depth of field and defocus in the form of a defocus map. This approach enables the recovery of fully focused images through multi-scale depth-of-field cross-stage fusion. By leveraging global information features ...
Fusion stage.在FFM的第二阶段,也就是融合阶段,使用简单的通道嵌入来合并两条路径的特征,这是通过1×1卷积层实现的。此外,本文认为在这样的通道级融合过程中,周围区域的信息也应该被用于鲁棒的RGB-X分割。因此,受Mix-FFN和ConvMLP中的的启发,增加了一个深度卷积层DWConv_{3 \times 3},实现了一个跨接结构。
(2011). Center-surround divergence of feature statistics for salient object detection. In 2011 International conference on computer vision (pp. 2214–2219). IEEE. Lan, X., Gu, X., & Gu, X. (2022). Mmnet: Multi-modal multi-stage network for rgb-t image semantic segmentation. Applied ...
In this study, a multi-feature cross-fusion model, HemoFuse, for hemolytic peptide identification is proposed. The feature vectors of peptide sequences are transformed by word embedding technique and four hand-crafted feature extraction methods. We apply multi-head cross-attention mechanism to ...
Currently, multimodal metaphor recognition is in an active exploration stage as an emerging direction in natural language processing. Although preliminary models [6, 7] have emerged, these models still have some roughness. First, there is a lack of effective fusion algorithms to integrate multi-sourc...
During the bidirectional feature fusion process, the spectral information contained in each modality is learned. Additionally, we design Residual Multiplicative Connections (RMC) to update the fused features at each layer. At the decoding stage, we utilize a Feature Pyramid Aggregation Network (FPN) ...