公开(公告)号：US20240070516A1

公开(公告)日：2024-02-29

申请号：US17822029

申请日：2022-08-24

Applicant: ADOBE INC.

Inventor： Parth Shailesh PATEL ,  Ashutosh MEHRA

IPC: G06N20/00 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/04

Abstract: Systems and methods for machine learning context based confidence calibration are disclosed. In one embodiment, a processing logic may obtain an image frame; generate, with a first machine learning model, a confidence score, a bounding box, and an instance embedding corresponding to an object instance inferred from the image frame; and compute, with a second machine learning model, a calibrated confidence score for the object instance based on the instance embedding, the confidence score, and the bounding box.

公开(公告)号：US20230085687A1

公开(公告)日：2023-03-23

申请号：US17991249

申请日：2022-11-21

Applicant: ADOBE INC.

Inventor： Ashutosh MEHRA ,  Vlad Ion MORARIU ,  Kajal GUPTA ,  Jayant Vaibhav SRIVASTAVA ,  Curtis Michael WIGINGTON ,  Tushar TIWARI

IPC: G06V30/414 ,  G06N3/02 ,  G06K9/62 ,  G06N20/00

Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.