公开(公告)号：US11790558B1

公开(公告)日：2023-10-17

申请号：US17363504

申请日：2021-06-30

Applicant: Amazon Technologies, Inc.

Inventor： Guha Balakrishnan ,  Raghu Deep Gadde ,  Pietro Perona ,  Aleix Margarit Martinez

IPC: G06N20/00 ,  G06T7/00 ,  G06V10/75 ,  G06F18/214

CPC classification number: G06T7/97 ,  G06F18/214 ,  G06N20/00 ,  G06V10/76

Abstract: Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.

公开(公告)号：US12299896B1

公开(公告)日：2025-05-13

申请号：US17702202

申请日：2022-03-23

Applicant: Amazon Technologies, Inc.

Inventor： Qianli Feng ,  Raghu Deep Gadde ,  Pietro Perona ,  Aleix Margarit Martinez

IPC: G06T3/14 ,  G06N3/045 ,  G06T7/187 ,  G06V10/74 ,  G06V10/774

Abstract: Described herein is a computer-implemented method for generating a synthetic image. An input image can be received by a computing device. A representation of the input image on an image approximation manifold can be identified by inputting the input image into a machine learning model. The image approximation manifold can be defined by the machine learning model. A local region of the image approximation manifold can be modified relative to the first representation to generate a modified image approximation manifold. The modified image approximation manifold can include a second representation of the input image. A synthetic image can be generated based on the modified image approximation manifold. A rendering of the synthetic image can be caused on a display.

公开(公告)号：US11501210B1

公开(公告)日：2022-11-15

申请号：US16698705

申请日：2019-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Fedor Zhdanov ,  Siddharth Vivek Joshi ,  Prateek Sharma ,  Alisa V. Shinkorenko ,  Warren Barkley ,  Stefano Stefani ,  Krzysztof Chalupka ,  Pietro Perona

IPC: G06N20/00 ,  G06N5/04

Abstract: A request associated with reviewing content for a field of interest is received. A confidence is determined associated with the content including the field of interest. A machine learning (ML) model determines a first confidence associated with the content includes the field of interest. The field of interest is transmitted for review in instances where the first confidence is less than a confidence threshold. After review, an indication associated with a reviewer reviewing the content and the first confidence associated with the ML model identifying the field of interest is updated to a second confidence.

公开(公告)号：US11429813B1

公开(公告)日：2022-08-30

申请号：US16697662

申请日：2019-11-27

Applicant: Amazon Technologies, Inc.

Inventor： Avinash Aghoram Ravichandran ,  Rahul Bhotika ,  Stefano Soatto ,  Pietro Perona ,  Hao Yang

IPC: G06K9/62 ,  G06N20/00

Abstract: This disclosure describes automatically selecting and training one or more models for image recognition based upon training and testing (validation) data provided by a user. A service provider network includes a recognition service that may use models to process images and videos to recognize objects in the images and videos, features on the objects in the images and videos, and/or locate objects in the images and videos. The service provider network also includes a model selection and training service that may select one or more modeling techniques based on the objectives of the user and/or the amount of data provided by the user. Based on the selected modeling technique, the model selection and training service selects and trains one or more models for use by the recognition service to process images and videos using the training data. The trained model may be tested and validated using the testing data.

公开(公告)号：US11048979B1

公开(公告)日：2021-06-29

申请号：US16370706

申请日：2019-03-29

Applicant: Amazon Technologies, Inc.

Inventor： Fedor Zhdanov ,  Siddharth Joshi ,  Sankalp Srivastava ,  Rahul Sharma ,  Pietro Perona ,  Sindhu Chejerla

IPC: G06K9/00 ,  G06K9/62 ,  G06N20/00

Abstract: Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the dataset to be individually and manually labeled by human labelers.