To effectively promote the recognition performance, this paper presents a novel paralleled dual-branch attention-based spatio-temporal fusion network (PASTFNet). We jointly extract short- and long-range spatial
In this paper, a novel three-stream network spatiotemporal attention enhanced features fusion network for action recognition is proposed. Firstly, features fusion stream which includes multi-level features fusion blocks, is designed to train the two streams jointly and complement the two-stream network...
ST-CGNet: A spatiotemporal gesture recognition network with triplet attention and dual feature fusion doi:10.1016/j.patcog.2025.111767Dynamic gesture recognitionC3DGatedConvLSTMHuman–computer interactionInnovative ST-CGNet captures both short and long-term spatiotemporal gesture features.Dual Feature ...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the
ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations Urban data Ⅰ 1. Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization ...
It is able to effectively capture the correlation between different roads in the urban transportation network, and then extract the spatial features of traffic flow data. The fourth is the functional fusion module. Its function is to enhance features through a spatiotemporal attention mechanism and ...
Graph Neural Network for Traffic Forecasting: A Survey —— temporal GNN 摘要交通预测对于智能交通系统的成功与否至关重要。深度学习模型,包括卷积神经网络和递归神经网络,已广泛应用于交通预测问题,以模拟空间和时间依赖性。近年来,为了对交通系统中的图结构… 马东什么 2019CVPR_Trilinear Attention Sampling Network...
By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD...
每个像素位置的时空变化程度用attention使得变化和不变的像素位置分别分配较高和较低的权值。通过(4)得到预测日期的特征图,可以自适应地关注融合过程中的时空变化信息。最后,根据(5)对深度语义特征Ft和所得到的特征Fpre进行了集成。(Def是反卷积操作) 鉴别器 相对论平均鉴别器的中心思想是:当假样本的预测概率增加(...
In this work, we present a novel spatial decoupling attention network (SDA-TR) for driver action recognition in in-vehicle scenarios. SDA-TR exploits several scales in the skeletons for multi-frames by spatiotemporal decoupling and enhances the relations among different granularities by decoupling att...