公开(公告)号：US20220172456A1

公开(公告)日：2022-06-02

申请号：US17437238

申请日：2019-03-08

Applicant: Google LLC

Inventor： Jiang Wang ,  Jiyang Gao ,  Shengyang Dai

IPC: G06V10/764 ,  G06V10/84 ,  G06V10/20

Abstract: The present disclosure provides systems and methods that include or otherwise leverage an object detection training model for training a machine-learned object detection model. In particular, the training model can obtain first training data and train the machine-learned object detection model using the first training data. The training model can obtain second training data and input the second training data into the machine-learned object detection model, and receive as an output of the machine-learned object detection model, data that describes the location of a detected object of a target category within images from the second training data. The training model can determine mined training data based on the output of the machine-learned object detection model, and train the machine-learned object detection model based on the mined training data.

公开(公告)号：US11288719B2

公开(公告)日：2022-03-29

申请号：US16802864

申请日：2020-02-27

Applicant: Google LLC

Inventor： Yang Xu ,  Jiang Wang ,  Shengyang Dai

IPC: G06Q30/04 ,  G06K9/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for converting unstructured documents to structured key-value pairs. In one aspect, a method comprises: providing an image of a document to a detection model, wherein: the detection model is configured to process the image to generate an output that defines one or more bounding boxes generated for the image; and each bounding box generated for the image is predicted to enclose a key-value pair comprising key textual data and value textual data, wherein the key textual data defines a label that characterizes the value textual data; and for each of the one or more bounding boxes generated for the image: identifying textual data enclosed by the bounding box using an optical character recognition technique; and determining whether the textual data enclosed by the bounding box defines a key-value pair.

公开(公告)号：US10339419B2

公开(公告)日：2019-07-02

申请号：US16208518

申请日：2018-12-03

Applicant: Google LLC

Inventor： Yang Song ,  Jiang Wang ,  Charles J. Rosenberg

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06K9/66 ,  G06N20/00 ,  G06F16/51 ,  G06F16/583

Abstract: Methods, systems, and apparatus, for determining fine-grained image similarity. In one aspect, a method includes training an image embedding function on image triplets by selecting image triplets of first, second and third images; generating, by the image embedding function, a first, second and third representations of the features of the first, second and third images; determining, based on the first representation of features and the second representation of features, a first similarity measure for the first image to the second image; determining, based on the first representation of features and the third representation of features, a second similarity measure for the the first image to the third image; determining, based on the first and second similarity measures, a performance measure of the image embedding function for the image triplet; and adjusting the parameter weights of the image embedding function based on the performance measures for the image triplets.

公开(公告)号：US10181091B2

公开(公告)日：2019-01-15

申请号：US15504870

申请日：2015-06-19

Applicant: Google LLC

Inventor： Yang Song ,  Jiang Wang ,  Charles J. Rosenberg

IPC: G06K9/00 ,  G06K9/62 ,  G06F17/30 ,  G06K9/66 ,  G06N99/00 ,  G06N3/04

Abstract: Methods, systems, and apparatus, for determining fine-grained image similarity. In one aspect, a method includes training an image embedding function on image triplets by selecting image triplets of first, second and third images; generating, by the image embedding function, a first, second and third representations of the features of the first, second and third images; determining, based on the first representation of features and the second representation of features, a first similarity measure for the first image to the second image; determining, based on the first representation of features and the third representation of features, a second similarity measure for the first image to the third image; determining, based on the first and second similarity measures, a performance measure of the image embedding function for the image triplet; and adjusting the parameter weights of the image embedding function based on the performance measures for the image triplets.