	US 12,093,306 B2
	Automatically detecting user-requested objects in digital images
Scott Cohen, Sunnyvale, CA (US); Zhe Lin, Fremont, CA (US); and Mingyang Ling, San Francisco, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Mar. 28, 2023, as Appl. No. 18/191,651.
Application 18/191,651 is a continuation of application No. 16/518,810, filed on Jul. 22, 2019, granted, now 11,631,234.
Prior Publication US 2023/0237088 A1, Jul. 27, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 20/70 (2022.01); G06F 16/535 (2019.01); G06F 18/2113 (2023.01); G06F 18/24 (2023.01); G06V 10/20 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01)

CPC G06F 16/535 (2019.01) [G06F 18/2113 (2023.01); G06F 18/24 (2023.01); G06V 10/255 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/70 (2022.01)]

20 Claims

1. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

identifying a query that comprises a query object identification label indicating a query object to be detected in one or more digital images;

determining which path of a multi-path object detection pipeline comprising a first path for known objects and a second path for unknown objects to use for detecting the query object in the one or more digital images by:

selecting, based on determining whether the query object corresponds to a known object class or an unknown object class, an object class detection neural network from among a set of possible object class detection neural networks for classifying the query object, wherein the set of possible object class detection neural networks comprise a known object class detection neural network and an unknown object class detection neural network; and

utilizing the selected object class detection neural network to detect the query object within the one or more digital images.