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...
在时间维度上,对每个行人单独考虑,应用temporal Transformer抽取时许相关性; 即使是时许上的Transformer,也提供了比RNN更好的表现; 在空间维度上,引入TGConv--Transformer-based message passing graph convolution mechanism。相较于传统的图卷积抽取行人之间的交互关系,采用TGConv在高人群密度、复杂交互关系的情形下能...
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 ...
However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alle- viate these problems, we propose a novel Spatio-temporal Transformer ...
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 ...
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。
论文精读|2024[KDD]ImputeFormer: 用于广义时空补全的低秩诱导的Transformer ImputeFormer 21. Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks 链接:https://arxiv.org/abs/2406.08287 ACM链接:https://dl.acm.org/doi/abs/10.1145/3637528.3671912 ...
一、简介 1、目的 作者的目的是引进一个spatio-temporal sub-pixel convolution networks,能够处理视频图像超分辨,并且做到实时速度。还提出了一个将动作补偿...。 Spatial transformer networks可以推断两个图像间的映射参数,并且成功运用于无监督光流特征编码中,但还未有人尝试用其进行视频运动补偿。 作者用的结构是,...
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...
论文中首先提出的是self-attention,这个有区别与transformer中的self-attention,只是在各自辅助广告序列内部不同广告特征经过一个MLP输出一个权重,然后使用softmax进行归一化,最后根据不同权重进行sum pooling,公式如下图3: 图3 self-attention公式 这样的确某种程度上能获取到有效信息以及抑制噪音,缓解了第二个问题。但是...