IP Library › Granted Patent US 12,626,496
Granted Patent B2
US 12,626,496 · App. 18/161,666 · Granted May 12, 2026

Utilizing interactive deep learning to select objects in digital visual media

Inventors: Brian Price (San Jose, CA); Scott Cohen (Sunnyvale, CA); Mai Long (Portland, OR); Jun Hao Liew (Singapore, SG)
Assignee: Adobe Inc.
G06V10/82G06F18/40G06N3/045G06N3/084G06N5/01G06T7/11G06V10/255G06V10/26G06V10/454G06V10/945G06N3/044G06V10/248
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Quick Facts
Patent No.
US 12,626,496
App. No.
18/161,666
Granted
May 12, 2026
Kind
B2
Abstract

Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

Claims (49)

1 . A method comprising:

generating, utilizing a neural network, predicted pixels corresponding to training objects utilizing digital training images portraying the training objects and training indicators comprising pixels and indications of how the pixels correspond to the training objects by:

generating, for a digital training image of the digital training images, a training distance map by determining distances between pixels in the digital training image and a training indicator corresponding to the digital training image; and

generating the predicted pixels from the training distance map utilizing the neural network;

comparing the predicted pixels corresponding to the training objects with training ground truth masks; and

training the neural network by comparing the predicted pixels corresponding to the training objects with the training ground truth masks.

2 . The method of claim 1 , further comprising:

generating an additional training distance map for an additional digital training image of the digital training images; and

generating additional predicted pixels for the additional digital training image from the additional training distance map utilizing the neural network.

3 . The method of claim 2 , wherein generating the additional training distance map comprises determining distances between pixels in the additional digital training image and additional training indicator corresponding to the additional digital training image.

4 . The method of claim 1 , wherein a digital training image comprises a training object and further comprising generating the training indicators by generating a positive training indicator comprising at least one pixel of the digital training image that is part of the training object.

5 . The method of claim 4 , wherein generating the training indicators comprises generating a negative training indicator comprising a background pixel of the digital training image that is not part of the training object.

6 . The method of claim 5 , further comprising generating the negative training indicator by randomly sampling the background pixel from a plurality of background pixels that are not part of the training object in the digital training image.

7 . The method of claim 5 , further comprising generating the negative training indicator by sampling the background pixel from a plurality of background pixels based on a distance between the background pixel and another negative training indicator.

8 . The method of claim 1 , further comprising:

identifying a training object and an untargeted object in a digital training image of the digital training images; and

generating a negative training indicator by sampling a pixel from the untargeted object in the digital training image.

9 . A system comprising:

a memory component; and

one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising:

generating, utilizing a neural network, predicted pixels corresponding to training objects utilizing digital training images portraying the training objects and training indicators comprising pixels and indications of how the pixels correspond to the training objects by:

generating, for a digital training image of the digital training images, a training distance map by determining distances between pixels in the digital training image and a training indicator corresponding to the digital training image; and

generating the predicted pixels from the training distance map utilizing the neural network;

comparing the predicted pixels corresponding to the training objects with training ground truth masks; and

training the neural network by comparing the predicted pixels corresponding to the training objects with the training ground truth masks.

10 . The system of claim 9 , further comprising:

generating an additional training distance map for an additional digital training image of the digital training images; and

generating additional predicted pixels for the additional digital training image from the additional training distance map utilizing the neural network.

11 . The system of claim 9 , wherein a digital training image comprises a training object and further comprising generating the training indicators by generating a positive training indicator comprising at least one pixel of the digital training image that is part of the training object.

12 . The system of claim 11 , wherein generating the training indicators comprises generating a negative training indicator comprising a background pixel of the digital training image that is not part of the training object.

13 . The system of claim 12 , further comprising generating the negative training indicator by randomly sampling the background pixel from a plurality of background pixels that are not part of the training object in the digital training image.

14 . The system of claim 12 , further comprising generating the negative training indicator by sampling the background pixel from a plurality of background pixels based on a distance between the background pixel and another negative training indicator.

15 . The system of claim 9 , further comprising:

identifying a training object and an untargeted object in a digital training image of the digital training images; and

generating a negative training indicator by sampling a pixel from the untargeted object in the digital training image.

16 . A non-transitory computer-readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising:

generating, utilizing a neural network, predicted pixels corresponding to training objects utilizing digital training images portraying the training objects and training indicators comprising pixels and indications of how the pixels correspond to the training objects by:

generating, for a digital training image of the digital training images, a training distance map by determining distances between pixels in the digital training image and a training indicator corresponding to the digital training image; and

generating the predicted pixels from the training distance map utilizing the neural network;

comparing the predicted pixels corresponding to the training objects with training ground truth masks; and

training the neural network by comparing the predicted pixels corresponding to the training objects with the training ground truth masks.

17 . The non-transitory computer-readable medium of claim 16 , further comprising:

generating an additional training distance map for an additional digital training image of the digital training images; and

generating additional predicted pixels for the additional digital training image from the additional training distance map utilizing the neural network.

18 . The non-transitory computer-readable medium of claim 17 , wherein generating the additional training distance map comprises determining distances between pixels in the additional digital training image and additional training indicator corresponding to the additional digital training image.

19 . The non-transitory computer-readable medium of claim 16 , wherein a digital training image comprises a training object and further comprising generating the training indicators by generating a positive training indicator comprising at least one pixel of the digital training image that is part of the training object.

20 . The non-transitory computer-readable medium of claim 16 , further comprising:

identifying a training object and an untargeted object in a digital training image of the digital training images; and

generating a negative training indicator by sampling a pixel from the untargeted object in the digital training image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 30, 2023
From: PRICE, BRIAN; COHEN, SCOTT; LONG, MAI; LIEW, JUN HAO
To: ADOBE INC.
Reel/Frame 062536/0055 →
Continuity (4)
Division 16376704 · Apr 5, 2019
Continuation In Part 16216739 · Dec 11, 2018
Continuation 14945245 · Nov 18, 2015
Related Publication 20230177824A1 · Jun 8, 2023
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