A general multimodal attention unit for any number of modalities. Graphical models inspire it, i.e., it infers several attention beliefs via aggregated interaction messages.

**PRNet+** is a multi-task neural network for outdoor position recovery from measurement record (MR) data. PRNet+ develops a feature extraction module to learn common local-, short- and long-term spatio-temporal locality from heterogeneous MR samples, with a convolutional neural network (CNN), long short-term memory cells ([LSTM](https://paperswithcode.com/method/lstm)), and attention mechanisms. Specifically, PRNet+ 1) allows the various-length sequences of MR samples, such that the two components (CNN and LSTM) are able to capture spatial locality from the samples within each MR sequence, 2) exploits two attention mechanisms for the time-interval between neighbouring MR samples, together with the one between neighbouring MR sequences, to capture temporal locality, and 3) incorporates the detected transportation modes and predicted locations of heterogeneous MR data into a joint loss for better result.

PRNet+

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

Factor Graph Attention

**3D Dynamic Scene Graph**, or **DSG**, is a representation that captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatio-temporal relations among nodes.

Source	Factor Graph Attention
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com