Learning disentangled invariant representations for one-shot instance recognition
First Claim
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1. A method of one-shot joint instance and pose recognition in an artificial neural network, comprising:
- training a generator for generating an orbit, the generator trained using a two-branch encoder-decoder architecture that receives two images of two different objects in a same pose;
receiving a first instance of a reference object from a reference image, the reference object having a first identity and a first pose in the first instance;
generating, via the trained generator, a first orbit of the reference object comprising a plurality of additional poses including a second pose for the reference object; and
recognizing a second instance of an example object from an example image, the example object having the first identity and the second pose in the second instance.
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Abstract
A method of one-shot joint instance and pose recognition in an artificial neural network includes receiving a first instance of a reference object from a reference image. The reference object has a first identity and a first pose in the first instance. The method also includes generating a first orbit of the reference object comprising additional poses including a second pose for the reference object. The method further includes recognizing a second instance of an example object from an example image. The example object has the first identity and the second pose in the second instance.
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Citations
26 Claims
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1. A method of one-shot joint instance and pose recognition in an artificial neural network, comprising:
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training a generator for generating an orbit, the generator trained using a two-branch encoder-decoder architecture that receives two images of two different objects in a same pose; receiving a first instance of a reference object from a reference image, the reference object having a first identity and a first pose in the first instance; generating, via the trained generator, a first orbit of the reference object comprising a plurality of additional poses including a second pose for the reference object; and recognizing a second instance of an example object from an example image, the example object having the first identity and the second pose in the second instance. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An artificial neural network for one-shot joint instance and pose recognition, the artificial neural network comprising:
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a memory; and at least one processor coupled to the memory, the at least one processor configured; to train a generator for generating an orbit, the generator trained using a two-branch encoder-decoder architecture that receives two images of two different objects in a same pose; to receive a first instance of a reference object from a reference image, the reference object having a first identity and a first pose in the first instance; to generate, via the trained generator, a first orbit of the reference object comprising a plurality of additional poses including a second pose for the reference object; and to recognize a second instance of an example object from an example image, the example object having the first identity and the second pose in the second instance. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium having program code recorded thereon for one-shot joint instance and pose recognition in an artificial neural network, the program code executed by a processor and comprising:
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program code to train a generator for generating an orbit, the generator trained using a two-branch encoder-decoder architecture that receives two images of two different objects in a same pose; program code to receive a first instance of a reference object from a reference image, the reference object having a first identity and a first pose in the first instance; program code to generate, via the trained generator, a first orbit of the reference object comprising a plurality of additional poses including a second pose for the reference object; and program code to recognize a second instance of an example object from an example image, the example object having the first identity and the second pose in the second instance. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. An apparatus for one-shot joint instance and pose recognition in an artificial neural network, comprising:
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means for receiving a first instance of a reference object from a reference image, the reference object having a first identity and a first pose in the first instance; means for generating a first orbit of the reference object comprising a plurality of additional poses including a second pose for the reference object; means for recognizing a second instance of an example object from an example image, the example object having the first identity and the second pose in the second instance; and means for training the means for generating via a two-branch encoder-decoder architecture that receives two images of two different objects in a same pose. - View Dependent Claims (23, 24, 25, 26)
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Specification