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US 12,405,997 B2Granted
Parallel prediction of multiple image aspects
AssigneeeBay Inc.
InventorsMohammadhadi Kiapour, Ajinkya Gorakhnath Kale, Robinson Piramuthu, Licheng Yu
Life of this patent
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Abstract
Example embodiments that analyze images to characterize aspects of the images rely on a same neural network to characterize multiple aspects in parallel. Because additional neural networks are not required for additional aspects, such an approach scales with increased aspects.
Claims19
A method comprising:
- receiving, by a network-based marketplace from a user device, a search query for an item listing of the network-based marketplace, the search query including an input image depicting an item;
- identifying, by a machine learning system, matches between an input semantic vector representing the input image and one or more image vectors of one or more images from a publication corpus;
- identifying a probability that the input image is associated with an aspect value that characterizes the item depicted in the input image based at least in part on the one or more image vectors;
- adding metadata comprising the aspect value to the input image responsive to the probability that the input image is associated with the aspect value exceeding a threshold;
- and
- transmitting, to the user device, a set of one or more search results responsive to the search query, wherein the set of one or more search results are identified based at least in part on the metadata and the input image.
3 more elements
A system comprising:
- one or more hardware processors;
- and
- a memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:
- receiving, from a user device, a search query for an item listing of a network-based marketplace, the search query including an input image depicting an item;
- identifying, by a machine learning system, matches between an input semantic vector representing the input image and one or more image vectors of one or more images from a publication corpus;
- identifying a probability that the input image is associated with an aspect value that characterizes the item depicted in the input image based at least in part on the one or more image vectors;
- adding metadata comprising the aspect value to the input image responsive to the probability that the input image is associated with the aspect value exceeding a threshold;
- and
- transmitting, to the user device, a set of one or more search results responsive to the search query, wherein the set of one or more search results are identified based at least in part on the metadata and the input image.
6 more elements
A non-transitory computer readable storage medium comprising instructions that when executed by one or more processors cause a system to perform operations comprising:
- receiving, from a user device, a search query for an item listing of a network- based marketplace, the search query including an input image depicting an item;
- identifying, by a machine learning system, matches between an input semantic vector representing the input image and one or more image vectors of one or more images from a publication corpus;
- identifying a probability that the input image is associated with an aspect value that characterizes the item depicted in the input image based at least in part on the one or more image vectors;
- adding metadata comprising the aspect value to the input image responsive to the probability that the input image is associated with the aspect value exceeding a threshold;
- and
- transmitting, to the user device, a set of one or more search results responsive to the search query, wherein the set of one or more search results are identified based at least in part on the aspect value metadata and the input image.
3 more elements
Dependencies are inferred from the claim text.