Temporal attention can be seen as a dynamic time selection mechanism determining when to pay attention, and is thus usually used for video processing.

**VDO-SLAM** is a feature-based stereo/RGB-D dynamic SLAM system that leverages image-based semantic information to simultaneously localise the robot, map the static and dynamic structure, and track motions of rigid objects in the scene. Input images are first pre-processed to generate instance-level object segmentation and dense optical flow. These are then used to track features on static background structure and dynamic objects. Camera poses and object motions estimated from feature tracks are then refined in a global batch optimisation, and a local map is maintained and updated with every new frame.

VDO-SLAM

VDO-SLAM: A Visual Dynamic Object-aware SLAM System

Temporal attention

Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification

**Segmentation Transformer**, or **SETR**, is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based segmentation model. The transformer-alone encoder treats an input image as a sequence of image patches represented by learned patch embedding, and transforms the sequence with global self-attention modeling for discriminative feature representation learning. Concretely, we first decompose an image into a grid of fixed-sized patches, forming a sequence of patches. With a linear embedding layer applied to the flattened pixel vectors of every patch, we then obtain a sequence of feature embedding vectors as the input to a transformer. Given the learned features from the encoder
transformer, a decoder is then used to recover the original image resolution. Crucially there is no downsampling in spatial resolution but global context modeling at every layer of the encoder transformer.

Source	Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com