Forming a dataset for fully-supervised learning

A device comprising:

• a non-transitory storage having stored thereon a data structure, the data structure comprising a computer program including instructions for performing a computer-implemented method of signal processing that when executed by processing circuitry causes the processing circuitry to be configured to:
• obtain images;
• for each respective one of at least a subset of the images:
• apply a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization, and
• determine, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization;
• and
• form a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.

A device comprising:

• a non-transitory storage having stored thereon a data structure, the data structure comprising a dataset formed by a computer-implemented method of signal processing that when executed by processing circuitry causes the processing circuitry to be configured to:
• obtain images;
• for each respective one of at least a subset of the images:
• apply a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization, and
• determine, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization;
• and
• form a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.

A device comprising:

• a non-transitory storage having stored thereon a data structure, the data structure comprising a fully-supervised learnt function learnable according to a computer-implemented method of signal processing that when executed by processing circuitry causes the processing circuitry to be configured to:
• obtain images;
• for each respective one of at least a subset of the images:
• apply a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization, and
• determine, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization;
• and
• form a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.