IP Library Granted Patent US 10,896,342
Granted Patent B2
US 10,896,342 · App. 16/189,974 · Granted Jan 19, 2021

Spatio-temporal action and actor localization

Inventors: Kirill Gavrilyuk (Amsterdam, NL); Amir Ghodrati (Amsterdam, NL); Zhenyang Li (Amsterdam, NL); Cornelis Gerardus Maria Snoek (Volendam, NL)
Assignee: Qualcomm Incorporated
G06K9/3241G06F16/3344G06F16/73G06K9/00255G06K9/00281G06T7/215G06T7/73G11B27/00G06T2207/10016G06T2207/20084
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Quick Facts
Patent No.
US 10,896,342
App. No.
16/189,974
Granted
Jan 19, 2021
Kind
B2
Abstract

A method of pixel-wise localization of an actor and an action in a sequence of frames includes receiving a natural language query describing the action and the actor. The method also includes receiving the sequence of frames. The method further includes localizing the action and the actor in the sequence of frames based on the natural language query.

Claims (57)

1. A method of pixel-wise localization of an actor and an action in a sequence of frames, comprising:

generating a first set of filters based on a natural language query describing the action and the actor;

generating a visual representation for each frame of the sequence of frames;

generating a response map for each frame of the sequence of frames based on a convolution of the first set of filters and the visual representation of each frame;

generating a second set of dynamic filters based on the natural language query and the response map;

labeling pixels in each frame of the sequence of frames based on a convolution of the second set of dynamic filters and an up-sampled visual representation of each frame; and

localizing the action and the actor in the sequence of frames based on the labeled pixels.

2. The method of claim 1 , further comprising:

up-sampling a resolution of the visual representation;

and

convolving the up-sampled visual representation with the second set of dynamic filters.

3. The method of claim 2 , further comprising repeating the up-sampling and the convolving for a set of resolutions of the visual representation.

4. The method of claim 2 , in which the first set of filters and the second set of dynamic filters are two-dimensional or three-dimensional filters.

5. The method of claim 1 , further comprising controlling an apparatus based on the localized action and actor.

6. An apparatus for pixel-wise localization of an actor and an action in a sequence of frames, the apparatus comprising:

means for generating a first set of filters based on a natural language query describing the action and the actor;

means for generating a visual representation for each frame of the sequence of frames;

means for generating a response map for each frame of the sequence of frames based on a convolution of the first set of filters and the visual representation of each frame;

means for generating a second set of dynamic filters based on the natural language query and the response map;

means for labeling pixels in each frame of the sequence of frames based on a convolution of the second set of dynamic filters and an up-sampled visual representation of each frame; and

means for localizing the action and the actor in the sequence of frames based on the labeled pixels.

7. The apparatus of claim 6 , further comprising:

means for up-sampling a resolution of the visual representation;

and

means for convolving the up-sampled visual representation with the second set of dynamic filters.

8. The apparatus of claim 7 , further comprising means for repeating up-sampling and convolving for a set of resolutions of the visual representation.

9. The apparatus of claim 7 , in which the first set of filters and the second set of dynamic filters are two-dimensional or three-dimensional filters.

10. The apparatus of claim 6 , further comprising means for controlling the apparatus based on the localized action and actor.

11. An apparatus for pixel-wise localization of an actor and an action in a sequence of frames, the apparatus comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor configured:

to generate a first set of filters based on a natural language query describing the action and the actor;

to generate a visual representation for each frame of the sequence of frames;

to generate a response map for each frame of the sequence of frames based on a convolution of the first set of filters and the visual representation of each frame;

to generate a second set of dynamic filters based on the natural language query and the response map;

to label pixels in each frame of the sequence of frames based on a convolution of the second set of dynamic filters and an up-sampled visual representation of each frame; and

to localize the action and the actor in the sequence of frames based on the labeled pixels.

12. The apparatus of claim 11 , in which the at least one processor is further configured to:

up-sample a resolution of the visual representation;

and

convolve the up-sampled visual representation with the second set of dynamic filters.

13. The apparatus of claim 12 , in which the at least one processor is further configured to up-sample and convolve for a set of resolutions of the visual representation.

14. The apparatus of claim 12 , in which the first set of filters and the second set of dynamic filters are two-dimensional or three-dimensional filters.

15. The apparatus of claim 11 , in which the at least one processor is further configured to control the apparatus based on the localized action and actor.

16. A non-transitory computer-readable medium having program code recorded thereon for pixel-wise localization of an actor and an action in a sequence of frames, the program code executed by a processor and comprising:

program code to generate a first set of filters based on a natural language query describing the action and the actor;

program code to generate a visual representation for each frame of the sequence of frames;

program code to generate a response map for each frame of the sequence of frames based on a convolution of the first set of filters and the visual representation of each frame;

program code to generate a second set of dynamic filters based on the natural language query and the response map;

program code to label pixels in each frame of the sequence of frames based on a convolution of the second set of dynamic filters and an up-sampled visual representation of each frame; and

program code to localize the action and the actor in the sequence of frames based on the labeled pixels.

17. The non-transitory computer-readable medium of claim 16 , in which the program code further comprises:

program code to up-sample a resolution of the visual representation; and

program code to convolve the up-sampled visual representation with the second set of dynamic filters.

18. The non-transitory computer-readable medium of claim 17 , in which the program code further comprises program code to up-sample and program code to convolve for a set of resolutions of the visual representation.

19. The non-transitory computer-readable medium of claim 17 , in which the first set of filters and the second set of dynamic filters are two-dimensional or three-dimensional filters.

20. The non-transitory computer-readable medium of claim 16 , in which the program code further comprises program code to control an apparatus based on the localized action and actor.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2019
From: GAVRILYUK, KIRILL; GHODRATI, AMIR; LI, ZHENYANG; SNOEK, CORNELIS GERARDUS MARIA
To: UNIVERSITEIT VAN AMSTERDAM
Reel/Frame 050883/0855 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2019
From: UNIVERSITEIT VAN AMSTERDAM
To: QUALCOMM TECHNOLOGIES, INC.
Reel/Frame 050883/0864 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2019
From: QUALCOMM TECHNOLOGIES, INC.
To: QUALCOMM INCORPORATED
Reel/Frame 050883/0899 →
Continuity (2)
Provisional Application 62586071 · Nov 14, 2017
Related Publication 20190147284A1 · May 16, 2019
Cited By (2)
US 12,596,747 US 12,682,641