Temporal Transformer Stream (T-TR):相反,时间流侧重于发现帧间时间关系。与 S-TR 流类似,在每个 T-TR 层内,一个标准的图卷积子模块 [16] 之后是提出的 Temporal Self-Attention 模块,即 T-TR(x) = TSA(GCN(x) ))。在这种情况下,TSA 对沿所有时间维度(例如,所有左脚或所有右手)链接相同关节的图进行...
这里介绍一篇最近的arXiv论文:“Spatial-Temporal Transformer for Dynamic Scene Graph Generation“,作者来自德国汉诺威大学和荷兰特温特大学。 从视频生成动态场景图(Dynamic scene graph), 比从图像生成场景图更具挑战性,因为目标之间的动态关系和帧之间的时间依赖性需要更丰富的语义解释。如图显示了图像和视频生成scene ...
2.Spatial-Temporal Transformer Network 这是STTN的核心部分,通过一个多头 patch-based attention模块沿着空间和时间维度进行搜索。transformer的不同头部计算不同尺度上对空间patch的注意力。这样的设计允许我们处理由复杂的运动引起的外观变化。例如,对大尺寸的patch(例如,帧大小H×W)旨在修复固定的背景;对小尺寸的patch...
In this work, we propose a novel Spatial-Temporal Transformer network (ST-TR) which models dependencies between joints using the Transformer self-attention operator. In our ST-TR model, a Spatial Self-Attention module (SSA) is used to understand intra-frame interactions between different body ...
Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks 基于骨骼通过时空变换网络的行为识别 CVPR2020 未解决的问题:有效编码3D骨骼下面的潜在信息,尤其是从关节运动模式以及其相关性中提取有效信息时,诸如“拍手”之类的动作在人体骨骼中未链接的身体关节之间的相关性(例...spatial transformer ...
To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial鈥搕emporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-...
1)Multi-view Self Attention 在每个视图中,特征在每个时间成对交互。为了便于具体描述,我们以短距离观为例。 然后,我们将时间attention与该值进行加权,得到一个新的特征表示的短距离视图 Global Temporal Attention 我们采用自我注意机制来模拟所有时间步骤的相关性。
However, traffic information has complex nonlinearity and dynamic spatial-temporal dependencies due to human mobility, bringing new traffic forecasting challenges. This paper proposed a graph spatial-temporal transformer network for traffic prediction (GSTTN) to cope with the above problems. Specifically, ...
Temporal Transformer implementation corresponds toST-TR/code/st_gcn/net/temporal_transformer.py. Set in/config/st_gcn/nturgbd/train.yaml: attention: False tcn_attention: True only_attention: True all_layers: False to run the temporal transformer stream (T-TR-stream). ...
12 Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting 13 Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting 14 Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity 15...