公开(公告)号：US20220147743A1

公开(公告)日：2022-05-12

申请号：US17226584

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Donna Roy ,  Suraj Kothawade ,  Elmar Haussmann ,  Jose Manuel Alvarez Lopez ,  Michele Fenzi ,  Christoph Angerer

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/72 ,  G06N3/08

Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.

公开(公告)号：US20230368079A1

公开(公告)日：2023-11-16

申请号：US17740935

申请日：2022-05-10

Applicant: NVIDIA Corporation

Inventor： Christoph Angerer ,  Devansh Bisla ,  Eugen Sawin ,  Elmar Haussmann ,  Eric Yang

IPC: G06N20/20 ,  B25J9/16 ,  G06F16/387 ,  G06N3/04

CPC classification number: G06N20/20 ,  B25J9/163 ,  B25J9/1697 ,  G06F16/387 ,  G06N3/0454

Abstract: In various examples, a cell model that partitions a geographic region into one or more cells is used to determine clusters of cell which share similarities. Sensor data is provided to one or more machine learning models trained to classify the sensor data to one or more cells of the cell model. Based on classifying sensor data to cells of a cell model, similarities between pairings of cells of the cell model may be determined and used to form clusters of the cell which are sufficiently similar in order to aid in the curation of training data used to train machine learning models in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

公开(公告)号：US12272148B2

公开(公告)日：2025-04-08

申请号：US17226584

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Donna Roy ,  Suraj Kothawade ,  Elmar Haussmann ,  Jose Manuel Alvarez Lopez ,  Michele Fenzi ,  Christoph Angerer

IPC: G06V20/56 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06N3/08 ,  G06V30/262

Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.