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AssigneeSRI International
InventorsBogdan Calin Mihai Matei, Xiyang Dai, John Benjamin Southall, Nhon Hoc Trinh, Harpreet Sawhney
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Techniques are disclosed for identifying discriminative, fine-grained features of an object in an image. In one example, an input device receives an image. A machine learning system includes a model comprising a first set, a second set, and a third set of filters. The machine learning system applies the first set of filters to the received image to generate an intermediate representation of the received image. The machine learning system applies the second set of filters to the intermediate representation to generate part localization data identifying sub-parts of an object and one or more regions of the image in which the sub-parts are located. The machine learning system applies the third set of filters to the intermediate representation to generate classification data identifying a subordinate category to which the object belongs. The system uses the part localization and classification data to perform fine-grained classification of the object.
A system for identifying discriminative, fine-grained features of an object in an image, comprising:
A method for identifying discriminative, fine-grained features of an object in an image, the method comprising:
A non-transitory computer-readable medium comprising instructions that, when executed, cause processing circuitry of a computation device to execute a machine learning system configured to identify discriminative, fine-grained features of an object in an image, wherein the machine learning system is configured to:
A method for training a first set of filters, a second set of filters, and a third set of filters of a model of a machine learning system, executed by processing circuitry of a computation engine, to identify discriminative, fine-grained features of an object in an image, the method comprising:
Dependencies are inferred from the claim text.