Method and system for training a neural network to be used for semantic instance segmentation

A method for training iteratively a neural network to be used for semantic instance segmentation, wherein, for each iteration, the neural network outputs a vector (10, 20, 30) for each pixel of a template image, wherein the template image comprises predefined elements each associated with pixels of the template image and the corresponding vectors, characterized in that training the neural network is performed using a loss function defined so that the loss function decreases until reaching a target value at least when: - for each vector belonging to an element, the distance between the vector and a center of the vectors of this element decreases, and - the distances between all the centers of the vectors of each element increase. The invention also concerns a method for semantic instance segmentation and corresponding systems.

Patent Publication Number: WO2019015785

Year filing: 2017

Year approval: 2020

Year publication: 2019

Status: Requested

Technology domains: Computer technology

Validated for IOF-key: Yes

Attributed to: Associatie KULeuven