Automatic caption generation with attention mechanisms aims at generating more descriptive captions containing coarser to finer semantic contents in the image. In this work, we use an encoder-decoder framework employing Wavelet transform based Convolutional Neural Network (WCNN) with two level discrete w...
to assess the predictive performance of MNCLCDA, we compare our model with six other advanced models in the field of bioinformatics. These include GATECDA [17], MNGACDA [18], LAGCN [45], MKGCN [46], CRPGCN [47] and
There are primarily two kinds of temporal attention: hard attention and soft attention. Hard attention is nondifferentiable [15]; therefore the attention weights need to be trained by reinforcement learning, which makes it difficult to be integrated into our model. On the contrary, soft attention ...
2024-07-14 MambaForGCN: Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis Adamu Lawan et.al. 2407.10347 null 2024-07-14 STGFormer: Spatio-Temporal GraphFormer for 3D Human Pose Estimation in Video Yang Liu et.al. 2407.10099...
Following the same procedure as our previous work [9], we constructed a graph to train the GCNNs model from discovery and validation cohorts, comprised of low-depth WGBS cfDNA samples of five cancer types (Dataset 4). The overall framework was depicted in [9]. The discovery cohort was then...
for classifying the outputs according to the labels.Since we are not classifying the nodes, we can extract the final node weights from the penultimate layer . This allows the projection of the embedding subspace in the VGCN kernel. The steps for using this(or any other variant of GCN kernel...
(GCN) [15,16], aims to overcome the limitations of traditional ML methods. In comparison to traditional ML methods, deep learning has stronger generalization ability and better performance in handling nonlinear problems [17]. CNN, a classical algorithm of deep learning, is widely used in many ...
Another way to handle this kind of data is by using Graph Convolutional Neural Networks (GCNNs). These networks convert a 3D point cloud to a graph and create an artificial lattice structure through the edges of the graph. This paper proposes a new methodology that fuses the geometric ...
2023-10-25 MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network Soroush Mehraban et.al. 2310.16288 link 2023-10-25 TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer Xiao Lin et.al. 2310.16279 null 2023-10-23 Converting Depth Images and Point...
A GCN is an extension of convolution on the graph domain. The GCN approaches have been applied to address biological problems such as predicting protein functions [19, 20]. For single cell classification, this paper proposes a new GCN-based end-to-end multimodal deep learning model. The ...