Understanding of deformable object manipulations such as textiles is a challenge due to the complexity and high dimensionality of the problem.
The input of the proposed network is a set of semantic graphs which store the spatial relations between subjects and objects in the scene.
On SiLago, we found solutions that achieve 97\% and 86\% of the maximum possible speedup and energy saving, with a minor increase in error.
This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.
Ranked #9 on 3D Semantic Segmentation on SemanticKITTI
Neural networks have been notorious for being computationally expensive.
SalsaNet segments the road, i. e. drivable free-space, and vehicles in the scene by employing the Bird-Eye-View (BEV) image projection of the point cloud.
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
Understanding continuous human actions is a non-trivial but important problem in computer vision.