To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are ...
Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose asimpleadaptive multi-modal fusion network for bra...
gated networkattention mechanismsemantic informationneural factorization machinesMany studies focusing on integrating reviews with ratings to improve recommendation performance have been quite successful. However, these works still face several shortcomings: (1) The importance of dynamically integrating review and...
An effective dual-branch hybrid approach for combining a CNN-based model and a Transformer-based model.A cross-weight feature fusion mechanism increases the accuracy and robustness of cancer classification.Dual-task learning with tailored loss functions improves the learning capability of the proposed mo...
network. Furthermore, the mamba module and C2f module are introduced to construct a bidirectional dense feedback network to enhance the transfer of contextual information in the neck part. Thirdly, an adaptive gate feature fusion network is proposed to improve the head part of YOLOv5 and enhance...
Based on this observation, we propose an Adaptive Context Network (ACNet) to capture the pixel-aware contexts by a competitive fusion of global context and local context according to different per-pixel demands. Specifically, when given a pixel, the global context demand is measured by the ...
Many bacteria use a type III secretion system (T3SS) to inject effector proteins into host cells. Selection and export of the effectors is controlled by a set of soluble proteins at the cytosolic interface of the membrane spanning type III secretion ‘in
An IF-Net is composed of a 3D convolution neural network encoder g(·) for multi-scale feature extrac- tion and a multi-layer perceptron (MLP) f (·) for implicit shape decoding. Given a point cloud sample P , it is first converted into a discrete voxel representation X ∈...
In this work, we propose a Multi-level Multi-task Representation Learning with Adaptive Fusion (MuReLAF) network to bridge the semantic gap among different modalities. Specifically, we design a modality adaptive fusion block to adjust modality contributions dynamically. Besides, we build a multi-...
This network incorporates a Gated Temporal Convolution Network (Gated TCN) that uses dilated causal convolutions at different granular levels to capture temporal dependencies in traffic flow. Additionally, we designed a spatial static–dynamic graph learning layer, which integrates static adaptive graph ...