近年来,transformer网络在NLP领域占据主导地位[43,10,26,52,50]。Transformer模型完全抛弃了递归性,而将注意力集中在跨时间step的关注上。该架构允许长期依赖建模和大规模并行训练。transformer结构也已成功应用于其他领域,如股票预测[30]、机器人决策[12]等。STAR将Transformer的思想应用于图序列。我们在一个具有挑战性...
图一、STAR中的temporal transformer以及spatial transformer。第一眼看上去右边这个和GAT很像。 2、模型设计 构建图:空间图中的边表示两个行人之间的距离小于一定阈值。 针对Temporal Transformer,就正常用Transformer即可。 【KEY】针对Spatial Transformer 首先有一个观察:self-attention可以看作是在无向全连图上传递信...
Spatio-temporal transformer networkSpatio-temporal flowSpatio-temporal samplerVideo super-resolutionVideo deblurringState-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames ...
Spatio-Temporal Transformer Network for Video Restoration外文学习材料.pdf,Spatio-Temporal Transformer Network for Video Restoration Tae Hyun Kim1,2, Mehdi S. M. Sajjadi1,3, Michael Hirsch1,4† , Bernhard Sch¨olkopf1 1 Max Planck Institute for Intellig
Learning Spatio-Temporal Transformer for Visual Tracking 论文 代码 搜索区域(Search Region):这是图像中的一块区域,通常大于或等于目标的实际大小。搜索区域为模型提供了足够的上下文来识别和定位目标。 初始模板(Initial Template):这是目标在序列开始时的一个参考图像或框,模型使用它来识别后续帧中的相同目标。
摘要: State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and...关键词: Spatio-temporal transformer network Spatio-temporal flow Spatio-temporal sampler Video ...
This paper proposes a novel multi-scale persistent spatiotemporal transformer (MSPSTT) for long-term urban traffic flow prediction to address the limitations of prior studies. The model adopts an encoder-decoder structure. The spatiotemporal decoder uses contextual information of the input data provided...
Processing spatiotemporal data sources with both high spatial dimension and rich temporal information is a ubiquitous need in machine intelligence. Recurrent neural networks in the machine learning domain and bio-inspired spiking neural networks in the n
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi Twitter {jcaballero, cledig, aaitken, aacostadiaz, johannes, zehanw, wshi}@twitter.com Abstract ...
Spatial-Temporal Transformer Networks for Traffic Flow Forecastingarxiv.org/abs/2001.02908 It presents a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture real-time traffic conditions as well as...