这部分的输出Z再加上一个帧之间的position embedding作为Temporal Transformer的输入。 Temporal Transformer Module:Spatial transformer已经对关节间的信息进行提取集合,做的是每帧内的局部运动学信息提取,Temporal Transformer要做的就是对序列帧的依赖进行建模,做的是全局信息的提取。这两个Transformer的结构是一样的,只是...
本文提出了时空变换器(Spatial-temporal Transformer ,STTran),一种由两个核心模块组成的神经网络:(1) 一个空间域编码器,采用输入帧提取空间上下文并推断帧内的视觉关系,以及 (2)时域解码器,将空间域编码器的输出作为输入,捕获帧之间的时间依赖性并推断动态关系。 此外,STtran 将不同长度的视频作为输入而无需剪辑...
这部分的输出Z再加上一个帧之间的position embedding作为Temporal Transformer的输入。 Temporal Transformer Module:Spatial transformer已经对关节间的信息进行提取集合,做的是每帧内的局部运动学信息提取,Temporal Transformer要做的就是对序列帧的依赖进行建模,做的是全局信息的提取。这两个Transformer的结构是一样的,只是...
这篇文章的目标是利用transformer实现真正的端到端多目标跟踪器的训练,这里的端到端是指给定一段图像序列,网络能够自动的处理轨迹的产生和终止以及生长。具体而言,提出的MO3TR模型使用temporal transformer实现每个轨迹历史特征的融合并预测当前时刻该轨迹的特征,另外使用spatial transformer刻画object之间的位置关系以及object...
Temporal and spatial correlationsGraph convolutional networkSpatial-temporal TransformerDEEPTraffic flow prediction is an important part of ITS, accurate traffic flow prediction plays a crucial role in the development of ITS. It can not only effectively avoid traffic problems such as traffic congestion, ...
We propose to resolve this issue by using a spatial-temporal transformer that naturally incorporates the spatial and temporal super resolution modules into a single model. Unlike CNN-based methods, we do not explicitly use separated building blocks for temporal inte...
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... ...
MotionAGFormer: enhancing 3D human pose estimation with a transformer-GCNFormer network. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp 6920–6930 Hassanin M, Khamiss A, Bennamoun M, Boussaid F, Radwan I. CrossFormer: cross spatio-temporal transformer ...
12 Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting 作者:Jing He,Junzhong Ji,Minglong Lei 关键词:气象预测,知识蒸馏 13 Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting ...
Effective learning of spatial-temporal information within a point cloud sequence is highly important for many down-stream tasks such as 4D semantic segmentation and 3D action recognition. In this paper, we propose a novel framework named Point Spatial-Temporal Transformer (PST2) to learn spatial-tem...