US 2023/0267335 A1 · SELF-SUPERVISED MULTI-SENSOR TRAINING AND SCENE ADAPTATION

Abstract

A sensing system including at least a first sensor sensing first data from a scene, at least a second sensor sensing second data from the scene, a first teacher/student machine learning subsystem employable by the first sensor to process the first data and a second student/teacher machine learning subsystem employable by the second sensor to process the second data, the first teacher/student machine learning subsystem being operative to teach the second student/teacher machine learning subsystem in a first instance, and the second student/teacher machine learning subsystem being operative to teach the first teacher/student machine learning subsystem in a second instance.

Claims21

Dependent claims

A sensing system comprising:

at least a first sensor sensing first data from a scene;
at least a second sensor sensing second data from said scene;
a first teacher/student machine learning subsystem employable by said first sensor to process said first data;
and
a second student/teacher machine learning subsystem employable by said second sensor to process said second data,
said first teacher/student machine learning subsystem being operative to teach said second student/teacher machine learning subsystem in a first instance, and
said second student/teacher machine learning subsystem being operative to teach said first teacher/student machine learning subsystem in a second instance.

4 more elements

A sensing system comprising:

at least a first sensor sensing first data from a scene;
at least a second sensor sensing second data from said scene, said first data being of a different type than said second data;
and
a Machine Learning Generative Module comprising at least one of:
(i) a generator sub-module operative to receive said first data sensed from said scene and to generate, using machine learning and based on said first data, a representation of said scene corresponding to said second type of data, and a paired data provider operative to provide to said generator sub-module pairs of mutually corresponding first type of data and second type of data previously sensed from said scene, said generator sub-module being operative to take into account said pairs of mutually corresponding previously sensed first and second types of data in generating said representation of said scene, and
(ii) a first generator sub-module operative to receive said first data and to generate, using machine learning and based on said first data, a representation of said scene corresponding to said second type of data, and a second generator sub-module operative to receive said representation of said scene generated by said first generator sub-module and said first data and to generate, using machine learning, a refined representation of said scene corresponding to said second type of data, based on said representation of said scene generated by said first generator sub-module and said first data.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application

18/005,232Filed 13 Jul 2021

Priority

13 Jul 2020

Published

24 Aug 2023

Term ends

Not estimatedThe source records don’t support an estimate.

Classification21 codes

G06N 3/0895
Learning methods: weakly supervised learning, e.g. semi-supervised or self-supervised learning
G06V 20/52
Context or environment of the image: surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks

Self-supervised multi-sensor training and scene adaptation

Life of this patent

Self-supervised multi-sensor training and scene adaptation

Life of this patent

Abstract

Claims21