Online adaptation of segmentation machine learning systems

An apparatus for performing online adaptation of one or more machine learning models, the apparatus comprising:

• at least one memory;
• and
• at least one processor coupled to the at least one memory and configured to:
• obtain features extracted from a first image by a machine learning model during inference;
• determine, using the machine learning model based on the features during inference, at least one of a plurality of keypoint estimates in the first image or a bounding region estimate associated with an object in the first image;
• generate one or more pseudo-labels based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• determine at least one self-supervised loss based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• adapt, based on the at least one self-supervised loss, one or more parameters of the machine learning model;
• and
• generate, using the machine learning model with the adapted one or more parameters, a segmentation mask for the first image or a second image;
• wherein the features are extracted from the first image by a feature extraction engine of the machine learning model;
• and
• wherein adapting, based on the at least one self-supervised loss, the one or more parameters of the machine learning model comprises adapting one or more parameters of the feature extraction engine.

A non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to:

• obtain features extracted from a first image by a machine learning model during inference;
• determine, using the machine learning model based on the features during inference, at least one of a plurality of keypoint estimates in the first image or a bounding region estimate associated with an object in the first image;
• generate one or more pseudo-labels based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• determine at least one self-supervised loss based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• adapt, based on the at least one self-supervised loss, one or more parameters of the machine learning model;
• and
• generate, using the machine learning model with the adapted one or more parameters, a segmentation mask for the first image or a second image;
• wherein the features are extracted from the first image by a feature extraction engine of the machine learning model;
• and
• wherein adapting, based on the at least one self-supervised loss, the one or more parameters of the machine learning model comprises adapting one or more parameters of the feature extraction engine.

An apparatus for performing online adaptation of one or more machine learning models, the apparatus comprising:

• means for obtaining features extracted from a first image by a machine learning model during inference;
• means for determining, by the machine learning model based on the features during inference, at least one of a plurality of keypoint estimates in the first image or a bounding region estimate associated with an object in the first image;
• means for generating one or more pseudo-labels based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• means for determining at least one self-supervised loss based on at least one of the plurality of keypoint estimates or the bounding region estimate;
• means for adapting, based on the at least one self-supervised loss, one or more parameters of the machine learning model;
• and
• means for generating, using the machine learning model with the adapted one or more parameters, a segmentation mask for the first image or a second image;
• wherein the features are extracted from the first image by a feature extraction engine of the machine learning model;
• and
• wherein adapting, based on the at least one self-supervised loss, the one or more parameters of the machine learning model comprises adapting one or more parameters of the feature extraction engine.