Forming a dataset for fully-supervised learning

1. A computer-implemented method of signal processing comprising:

obtaining images;

for each respective one of at least a subset of the images:

applying 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

determining, 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

forming 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,

wherein the localization of each respective annotation corresponds to one or more localizations outputted by the weakly-supervised learnt function,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is strictly superior to zero,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is superior to a strictly positive threshold, and

wherein the threshold has a value which depends on a mean number of objects in the images.

2. The method of claim 1 , wherein the object category respective to each respective annotation is the object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by the highest confidence score.

3. The method of claim 1 , wherein, for each respective image of at least a part of the subset:

the respective image is provided with respective initial labels, each initial label representing instantiation of a respective object category in the respective image, and

the label of each respective annotation of the respective image representing instantiation of a respective object category corresponding to an initial label of the respective image.

4. The method of claim 1 , wherein the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of a respective annotation are identified via a clustering algorithm.

5. The method of claim 1 , the weakly-supervised learnt function is learnt based on an initial dataset, the initial dataset including initial pieces of data, each initial piece of data including a respective image and a respective annotation, the annotation consisting of a respective set of labels, each label representing instantiation of a respective object category in the respective image.

6. The method of claim 1 , wherein the method further comprises learning a fully-supervised learnt function based on the formed dataset, the fully-supervised learnt function applying to images and 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.

7. 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,

wherein the localization of each respective annotation corresponds to one or more localizations outputted by the weakly-supervised learnt function,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is strictly superior to zero,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is superior to a strictly positive threshold, and

wherein the threshold has a value which depends on a mean number of objects in the images.

8. The device of claim 7 , wherein the non-transitory storage is computer-readable.

9. The device of claim 7 , wherein the non-transitory storage is a memory, the device further comprising processing circuitry coupled to the memory.

10. 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,

wherein the localization of each respective annotation corresponds to one or more localizations outputted by the weakly-supervised learnt function,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is strictly superior to zero,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is superior to a strictly positive threshold, and

wherein the threshold has a value which depends on a mean number of objects in the images.

11. The device of claim 10 , wherein the non-transitory storage is computer-readable.

12. The device of claim 10 , wherein the non-transitory storage is a memory, the device further comprising the processing circuitry.

13. 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,

wherein the localization of each respective annotation corresponds to one or more localizations outputted by the weakly-supervised learnt function,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is strictly superior to zero,

wherein the object category respective to each respective annotation is an object category having a probability of instantiation, at the one or more localizations outputted by the weakly-supervised learnt function that correspond to the localization of the respective annotation, which is represented by a respective confidence score which is superior to a strictly positive threshold, and

wherein the threshold has a value which depends on a mean number of objects in the images.

14. The device of claim 13 , wherein the non-transitory storage is computer-readable.

15. The device of claim 13 , wherein the non-transitory storage is a memory, the device further comprising the processing circuitry.