**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.

**GreedyNAS** is a one-shot [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. Previous methods held the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (eg, $7^{21}$). GreedyNAS eases the burden of supernet by encouraging focus more on evaluation of potentially-good candidates, which are identified using a surrogate portion of validation data. 

Concretely, during training, GreedyNAS utilizes a multi-path sampling strategy with rejection, and greedily filters the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. An exploration and exploitation policy is adopted by introducing an empirical candidate path pool.

GreedyNAS

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

VDO-SLAM

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

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

Source	VDO-SLAM: A Visual Dynamic Object-aware SLAM System
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com