公开(公告)号：US20240320493A1

公开(公告)日：2024-09-26

申请号：US18254634

申请日：2021-02-22

Applicant: Ahmet ISCEN ,  Andre Filgueiras de Araujo ,  Boqing Gong ,  Cordelia Luise SCHMID ,  Google LLC

Inventor： Ahmet Iscen ,  Andre Filgueiras de Araujo ,  Boqing Gong ,  Cordelia Luise Schmid

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: Class-balanced distillation can train recognition models with little to no bias even if the training dataset has a class imbalance. A two stage training process with instance sampling and class-balanced sampling can train the recognition model to recognize both head classes and tail classes. Moreover, one or more teacher classification models can be trained, and the knowledge can be distilled to a student classification model.

公开(公告)号：US10650042B2

公开(公告)日：2020-05-12

申请号：US16558852

申请日：2019-09-03

Applicant: Google LLC

Inventor： Andre Filgueiras de Araujo ,  Jiwoong Sim ,  Bohyung Han ,  Hyeonwoo Noh

IPC: G06K9/62 ,  G06F16/583 ,  G06K9/00 ,  G06K9/46 ,  G06K9/66

Abstract: Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application.

公开(公告)号：US20200004777A1

公开(公告)日：2020-01-02

申请号：US16558852

申请日：2019-09-03

Applicant: Google LLC

Inventor： Andre Filgueiras de Araujo ,  Jiwoong Sim ,  Bohyung Han ,  Hyeonwoo Noh

IPC: G06F16/583 ,  G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06K9/66

Abstract: Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application.