公开(公告)号：US20240281467A1

公开(公告)日：2024-08-22

申请号：US18649516

申请日：2024-04-29

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06F16/583 ,  G06F16/58 ,  G06F16/901 ,  G06F16/903 ,  G06F16/9032 ,  G06V10/75 ,  G06V10/764 ,  G06V10/84

CPC classification number: G06F16/583 ,  G06F16/5866 ,  G06F16/9024 ,  G06F16/9032 ,  G06F16/90348 ,  G06V10/751 ,  G06V10/764 ,  G06V10/84

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

公开(公告)号：US20230131935A1

公开(公告)日：2023-04-27

申请号：US17969505

申请日：2022-10-19

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Himanshu Rai ,  Yichao Lu

IPC: G06V10/764 ,  G06V10/77 ,  G06V10/774

Abstract: An object detection model and relationship prediction model are jointly trained with parameters that may be updated through a joint backbone. The offset detection model predicts object locations based on keypoint detection, such as a heatmap local peak, enabling disambiguation of objects. The relationship prediction model may predict a relationship between detected objects and be trained with a joint loss with the object detection model. The loss may include terms for object connectedness and model confidence, enabling training to focus first on highly-connected objects and later on lower-confidence items.

公开(公告)号：US20220318298A1

公开(公告)日：2022-10-06

申请号：US17848122

申请日：2022-06-23

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06F16/583 ,  G06F16/903 ,  G06F16/58 ,  G06F16/901 ,  G06F16/9032

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

公开(公告)号：US11995121B2

公开(公告)日：2024-05-28

申请号：US18216372

申请日：2023-06-29

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06K9/60 ,  G06F16/58 ,  G06F16/583 ,  G06F16/901 ,  G06F16/903 ,  G06F16/9032 ,  G06V10/75 ,  G06V10/764 ,  G06V10/84

CPC classification number: G06F16/583 ,  G06F16/5866 ,  G06F16/9024 ,  G06F16/9032 ,  G06F16/90348 ,  G06V10/751 ,  G06V10/764 ,  G06V10/84

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

公开(公告)号：US20230401252A1

公开(公告)日：2023-12-14

申请号：US18216372

申请日：2023-06-29

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06F16/583 ,  G06F16/903 ,  G06F16/58 ,  G06F16/901 ,  G06F16/9032 ,  G06V10/75 ,  G06V10/764 ,  G06V10/84

CPC classification number: G06F16/583 ,  G06F16/90348 ,  G06F16/5866 ,  G06F16/9024 ,  G06F16/9032 ,  G06V10/751 ,  G06V10/764 ,  G06V10/84

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

公开(公告)号：US11748400B2

公开(公告)日：2023-09-05

申请号：US17848122

申请日：2022-06-23

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06K9/60 ,  G06F16/583 ,  G06F16/903 ,  G06F16/58 ,  G06F16/901 ,  G06F16/9032 ,  G06V10/75 ,  G06V10/764 ,  G06V10/84

CPC classification number: G06F16/583 ,  G06F16/5866 ,  G06F16/9024 ,  G06F16/9032 ,  G06F16/90348 ,  G06V10/751 ,  G06V10/764 ,  G06V10/84

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

公开(公告)号：US20200159766A1

公开(公告)日：2020-05-21

申请号：US16592006

申请日：2019-10-03

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

IPC: G06F16/583 ,  G06F16/903 ,  G06F16/9032 ,  G06F16/901 ,  G06F16/58

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.