# US 2018/0322371 A1 · Forming a dataset for fully-supervised learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322371 A1 - Document ID: US20180322371A1 - Status: Published application - Kind: A1 - Application number: 15/973,165 - Filed: 2018-05-07 - Published: 2018-11-08 - Earliest claimed: 2017-05-05 - Assignees: Dassault Systemes - Applicants: Dassault Systemes - Inventors: Louis DUPONT DE DINECHIN; Asma REJEB SFAR - Web page: https://patentgrep.com/patent/US20180322371A1 ## CPC - G06V 10/7753: {Incorporation of unlabelled data, e.g. multiple instance learning [MIL]} - G06F 18/2155: {characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling} - G06F 18/2185: {the supervisor being an automated module, e.g. intelligent oracle} - G06F 18/23: Clustering techniques - G06F 18/28: Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06V 10/245: {by locating a pattern; Special marks for positioning} - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A computer-implemented method of signal processing comprises providing images. The method comprises 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. The method further comprises 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. The method further comprises 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. This improves the field of object detection. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the localization of each respective annotation corresponds to one or more localizations outputted by the weakly-supervised learnt function. ### Claim 3 (depends on 2) The method of claim 2, 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. ### Claim 4 (depends on 3) The method of claim 3, 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. ### Claim 5 (depends on 3) The method of claim 3, 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. ### Claim 6 (depends on 5) The method of claim 5, wherein the threshold has a value which depends on a mean number of objects in the images. ### Claim 7 (depends on 3) The method of claim 3, 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. ### Claim 8 (depends on 2) The method of claim 2, 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. ### Claim 9 (depends on 1) 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. ### Claim 10 (depends on 1) 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. ### Claim 11 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. ### Claim 12 (depends on 11) The device of claim 11, wherein the non-transitory storage is computer-readable. ### Claim 13 (depends on 11) The device of claim 11, wherein the non-transitory storage is a memory, the device further comprising processing circuitry coupled to the memory. ### Claim 14 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. ### Claim 15 (depends on 14) The device of claim 14, wherein the non-transitory storage is computer-readable. ### Claim 16 (depends on 14) The device of claim 14, wherein the non-transitory storage is a memory, the device further comprising the processing circuitry. ### Claim 17 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. ### Claim 18 (depends on 17) The device of claim 17, wherein the non-transitory storage is computer-readable. ### Claim 19 (depends on 17) The device of claim 17, wherein the non-transitory storage is a memory, the device further comprising the processing circuitry.