A ground robot explores an indoor space with a single floor and vertical walls, and collects a sequence of intensity images and 2D LiDAR datasets.
Following the fact that images of the same body region should share similar anatomical structures, and pixels of the same structure should have similar semantic patterns, we design a neural network to construct a local discriminative embedding space where pixels with similar contexts are clustered and dissimilar pixels are dispersed.
However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.
In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.
Ranked #8 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets.