公开(公告)号：US11841925B1

公开(公告)日：2023-12-12

申请号：US17117474

申请日：2020-12-10

Applicant: Amazon Technologies, Inc.

Inventor： Ria Chakraborty ,  Pranesh Bhimarao Kaveri ,  Rohit Kamal Saxena ,  Chaitra C N ,  C Manian Gandhi ,  Santosh Kumar Sahu

IPC: G06F18/2413 ,  G06N20/00 ,  G06F18/21

CPC classification number: G06F18/2413 ,  G06F18/2185 ,  G06N20/00

Abstract: Devices and techniques are generally described for content classification. In some examples, first item data representing a first item may be received. The first item data may include a plurality of prediction scores output by a machine learning model. Each prediction score of the plurality of prediction scores may be associated with a respective label of a plurality of labels. In some examples, a set of one or more labels among the plurality of labels may be predicted. The set of labels may be predicted as being applicable to the first item for classification of the first item. A determination may be made that the set of one or more labels represents a complete set of labels applicable to the first item. In some examples, the first item may be classified based on the set of one or more labels.

公开(公告)号：US11971955B1

公开(公告)日：2024-04-30

申请号：US17381833

申请日：2021-07-21

Applicant: Amazon Technologies, Inc.

Inventor： Ria Chakraborty ,  Madhur Popli ,  Rachit Lamba ,  Santosh Kumar Sahu ,  Rishi Kishore Verma

IPC: G06V10/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06T7/73 ,  G06V10/75 ,  G06F3/0482

CPC classification number: G06F18/2148 ,  G06F18/2155 ,  G06F18/2163 ,  G06F18/2178 ,  G06F18/22 ,  G06F18/2413 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06T7/73 ,  G06V10/751 ,  G06F3/0482 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/09

Abstract: Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.

公开(公告)号：US11947590B1

公开(公告)日：2024-04-02

申请号：US17476292

申请日：2021-09-15

Applicant: Amazon Technologies, Inc.

Inventor： Ria Chakraborty ,  Madhur Popli ,  Rishi Kishore Verma ,  Pranesh Bhimarao Kaveri

IPC: G06F16/00 ,  G06F16/538 ,  G06F16/54 ,  G06F18/24 ,  G06N3/045 ,  G06Q30/0601

CPC classification number: G06F16/538 ,  G06F16/54 ,  G06F18/24 ,  G06N3/045 ,  G06Q30/0641

Abstract: Embodiments of a contextualized visual search (CVS) system are disclosed capable of isolating target images of items that contain instances of a previously-unseen query image from a large database of target images. In embodiments, the system is used to implement an interactive query interface of an e-commerce portal, which allows the user to specify the query image (e.g. a logo) to be searched. The system converts the query image into a feature vector using a first machine learning model, and compares the feature vector to feature vectors of target images using a second machine learning model to find matching target images that contain an instance of the query image. The system then returns a query result indicating a list of items associated with matched target images. In embodiments, the query results may be ranked based on a set of personalized factors associated with the user.