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：US12229179B1

公开(公告)日：2025-02-18

申请号：US18515105

申请日：2023-11-20

Applicant: Amazon Technologies, Inc.

Inventor： Matthaeus Kleindessner ,  Christopher Michael Russell ,  Kailash Budhathoki ,  Ali Caner Turkmen ,  Siqi Deng ,  Varad Gunjal ,  Ashwin Swaminathan ,  Raghavan Manmatha ,  Hao Yang

IPC: G06F16/40 ,  G06F16/432 ,  G06F16/53

Abstract: The present disclosure generally relates to systems and methods for searching media content. In some implementation examples, a search system receives an input query, generates a query embedding of the input query, and generates a bias mitigation transformation associated with a sensitive attribute. Based on the query embedding and the bias mitigation transformation, the search system generates a transformed query embedding that suppresses at least a portion of the query embedding related to the sensitive attribute. Using the transformed query embedding, the search system executes a similarity search in a media embedding model to identify one or more media embeddings that are similar to the transformed query embedding and transmits the one or more media embeddings.