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...
但是，以往的工作，如ARG、SAM和Actor Transformer都仅以一个时空顺序对个体关系进行建模，即时间-空间（TS，Temporal- Spatial）或空间-时间（ST，Spatial-Temporal）。最近，来自悉尼科技大学、新加坡国立大学以及中科院深圳先进院等机构的作者发现，不同的时空建模顺序对于个体之间的关系模型，进一步对群体行为关系判别有...
block内的temporal transformer 处理过程Y^T_{l} = T(X^T_{l}),原料是输入X,经过T函数也就是temporal transformer得到\hat{Y}^T \in \mathbb{R}^{M \times d_g}和输入做残差得到Y^T = [\hat Y^T_1, ..., \hat Y^T_N],每点融合了时间信息。 spatial transformer:Position Encodings 首先输入...
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...
On the other hand, the temporal transformer is utilized to model long-range bidirectional temporal dependencies across multiple time steps. Finally, they are composed as a block to jointly model the spatial-temporal dependencies for accurate traffic prediction. Compared to existing works, the proposed...
基于LSTM与Transformer的地面沉降智能预测方法研究——以上海市为例. 时空信息学报, 31(1): 94-103Peng W X, Zhang D Y. 2024. Research on land subsidence intelligent prediction method based on LSTM and Transformer: A case st...
得益于Transformer构架的可扩展性,不同于SimVP框架的CNN Encoder-Decoder模型,对spatial和temporal的hidden dim以及block数都设置了不同的值,PredFormer对spatial和temporal GTB采用相同的固定的参数,因此只需要调整M的值,在比较少次数的调整后就可以达到最优性能。
However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that ...
Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation. Electronics. 2023; 12(12):2598. https://doi.org/10.3390/electronics12122598 Chicago/Turabian Style Shi, Dapai, Jingyuan Zhao, Zhenghong Wang, Heng Zhao, Junbin Wang, Yubo Lian, and Andrew F. Burke. ...
vivit import ViT v = ViT( image_size = 128, # image size frames = 16, # number of frames image_patch_size = 16, # image patch size frame_patch_size = 2, # frame patch size num_classes = 1000, dim = 1024, spatial_depth = 6, # depth of the spatial transformer temporal_depth ...