US 2026/0030880 A1 · RECURSIVE-TEMPORAL MODELS FOR AUTONOMOUS OR SEMI-AUTONOMOUS PERCEPTION SYSTEMS AND APPLICATIONS

Abstract

In various examples, machine learning models that benefit from temporal context while being computationally efficient to train and use are described herein. For instance, the disclosed systems and methods may apply a temporal series of images to a model and use intermediate features output from one or more backbone layers of the model as training data. In some examples, one or more recursive layers and/or one or more head layers of the model—or another model—may be trained using the training data by applying the intermediate features to the recursive layer(s). The recursive layer(s) may output a state representative of a temporal combination of the intermediate features, and the state may be applied to the head layer(s) to make one or more predictions. During inference, the recursive layer(s) may, in some examples, continuously update the state based on previous states of the recursive layer(s).

Claims20

Dependent claims

A method comprising:

performing one or more operations associated with a machine based at least on one or more outputs of a machine learning model corresponding to one or more first predictions associated with an environment, the machine learning model trained, at least, by:
applying, to one or more first layers of the machine learning model, training data including at least a temporal series of features corresponding to a temporal series of images;
applying, to one or more second layers of the machine learning model, state data generated using the one or more first layers, the state data representative of a combination of at least the temporal series of features and one or more additional features corresponding to a current image;
obtaining one or more second predictions using the one or more second layers to process at least a portion of the state data;
and
updating one or more parameters of the machine learning model based at least on an evaluation of the one or more second predictions with respect to ground truth data associated with at least the current image.

3 more elements

One or more processors comprising:

processing circuitry to cause performance of one or more operations of a machine based at least on one or more outputs of a neural network, the one or more outputs computed based at least on the neural network processing an instance of sensor data obtained using one or more sensors of the machine along with state data stored internal to the neural network, the state data computed using one or more temporal layers of the neural network and based at least on the neural network processing a plurality of instances of sensor data prior to the instance of sensor data.

Dependencies are inferred from the claim text.

Recursive-temporal models for autonomous or semi-autonomous perception systems and applications

Life of this patent

Recursive-temporal models for autonomous or semi-autonomous perception systems and applications

Life of this patent

Abstract

Claims20