Previous work cannot capture the fine-grained features of images, and those models bring a lot of noise during feature fusion. In this work, we propose a novel multimodal sentiment classification model based on gated attention mechanism. The image feature is used to emphasize the text segment by...
Therefore, we propose a novel Collaborative Attention-based Heterogeneous Gated Fusion Network (CHGFNet), which hierarchically fuses both optical and SAR features for land cover classification. More specifically, the CHGFNet consists of three main components: two-stream feature extractor, multimodal ...
Face recognitionDeep learningConvolutional block attention moduleSphereFaceCaricature recognition is a challenging problem, because there are typically geometric deformations between photographs and caricatures. It is nontrivial to learn discriminant large-margin features...doi...
Therefore, we use fusion residual as a fixed module for encoding and decoding. In addition, we found that the traditional defogging model based on the U-net network may cause some information losses in space. We have achieved effective maintenance of low-level feature information through the ...
Deep learning method for 6D object pose estimation based on RGB image and depth (RGB-D) has been successfully applied to robot grasping. The fusion of RGB
Joint Gated Co-Attention Based Multi-Modal Networks for Subregion House Price Prediction doi:10.1109/TKDE.2021.3093881Subregion house price predictionmulti-modal networksheterogeneous data fusionUrban housing price is widely accepted as an economic indicator which is of both business and research interest ...
we propose a gated position-sensitive axial attention mechanism where we introduce four gates that control the amount of information the positional embedding supply to key, query, and value. These gates are learnable parameters which make the proposed mechanism to be applied to any dataset of any ...
However, these works still face several shortcomings: (1) The importance of dynamically integrating review and interaction data features is typically ignored, yet treating these fusion features equally may lead to an incomplete understanding of user preferences. (2) Some forms of soft attention ...
Furthermore, with the fusion of ResNet and InceptionV4, He et al. [29] rendered the ResNeXt structure based on the group convolution. In the ResNeXt, all of the network routes shared the same topological structure, which reduced the algorithm complexity meanwhile greatly improved the image ...
Besides, attention generated by high-resolution shallow features can also rectify the noisy upsampled-feature generated by bilinear interpolation. The architecture of our proposed GPNet is shown in Fig. 2. We employ one GPM as segmentation head and three CLAMs in decoder. We conduct experiments ...