The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 22, 2022

Filed:

May. 14, 2020
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Zhe Lin, Fremont, CA (US);

Xiaohui Shen, San Jose, CA (US);

Mingyang Ling, San Jose, CA (US);

Jianming Zhang, Campbell, CA (US);

Jason Wen Yong Kuen, San Jose, CA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00671 (2013.01); G06K 9/00201 (2013.01); G06K 9/6256 (2013.01); G06N 3/04 (2013.01);
Abstract

In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes.


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