U.S. patent application number 16/114059 was filed with the patent office on 2019-01-03 for video understanding platform.
The applicant listed for this patent is Facebook, Inc.. Invention is credited to Merlyn Deng, Benoit F. Dumoulin, Dario Garcia Garcia, Balmanohar Paluri, Reena Philip.
Application Number | 20190005332 16/114059 |
Document ID | / |
Family ID | 62708427 |
Filed Date | 2019-01-03 |
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United States Patent
Application |
20190005332 |
Kind Code |
A1 |
Paluri; Balmanohar ; et
al. |
January 3, 2019 |
VIDEO UNDERSTANDING PLATFORM
Abstract
In one embodiment, a method includes accessing a video-content
object, determining a first feature vector representing the
video-content object using a first recognition module of a first
type based on an object in the video-content object, and
determining a second feature vector representing the video-content
object using a second recognition module of a second type based on
the first feature vector. The first type is different from the
second type. The method also includes determining a context of the
video-content object based on the second feature vector.
Inventors: |
Paluri; Balmanohar;
(Mountain View, CA) ; Dumoulin; Benoit F.; (Palo
Alto, CA) ; Deng; Merlyn; (Palo Alto, CA) ;
Philip; Reena; (Saratoga, CA) ; Garcia; Dario
Garcia; (Redwood City, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Family ID: |
62708427 |
Appl. No.: |
16/114059 |
Filed: |
August 27, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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15395511 |
Dec 30, 2016 |
10061985 |
|
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16114059 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 21/84 20130101;
H04N 21/44008 20130101; G06K 9/00677 20130101; G06K 9/00744
20130101; G06K 9/6267 20130101; H04N 21/25891 20130101; G06K 9/623
20130101; H04L 67/306 20130101; G06K 9/4604 20130101; H04N 21/4532
20130101; H04N 21/4826 20130101; G06K 9/00718 20130101; G06K 9/6215
20130101; H04N 21/4788 20130101; H04L 67/02 20130101; H04N 21/4394
20130101; G10L 15/26 20130101; H04L 43/045 20130101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; H04N 21/84 20110101 H04N021/84; H04N 21/4788 20110101
H04N021/4788; H04N 21/45 20110101 H04N021/45; H04N 21/44 20110101
H04N021/44; H04N 21/439 20110101 H04N021/439; H04N 21/258 20110101
H04N021/258; H04N 21/482 20110101 H04N021/482; G06K 9/62 20060101
G06K009/62; H04L 12/26 20060101 H04L012/26 |
Claims
1. A method comprising: by one or more computing devices, accessing
a video-content object; by one or more computing devices,
determining a first feature vector representing the video-content
object using a first recognition module of a first type based on an
object in the video-content object; by one or more computing
devices, determining a second feature vector representing the
video-content object using a second recognition module of a second
type based on the first feature vector, wherein the first type is
different from the second type; and by one or more computing
devices, determining a context of the video-content object based on
the second feature vector.
2. The method of claim 1, wherein: the first recognition module is
an audio-recognition module; the first feature vector represents a
predicted transcript of the video-content object, wherein the
transcript comprises text; and the second recognition module is a
text-recognition module.
3. The method of claim 1, wherein: the first recognition module is
a video-recognition module and the second recognition module is a
text-recognition module; the first recognition module is a
video-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is a
text-recognition module and the second recognition module is a
video-recognition module; the first recognition module is a
text-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is an
audio-recognition module and the second recognition module is a
video-recognition module; or the first recognition module is an
audio-recognition module and the second recognition module is a
text-recognition module.
4. The method of claim 1, wherein: the video-content object
corresponds to a node in a social graph of a social-networking
system; the social graph comprises a plurality of nodes and edges
connecting the nodes; and the context of the video-content object
is determined based on social-graph information based at least in
part on one or more nodes or edges connected to the node
corresponding to the video-content object, in addition to the
second feature vector.
5. The method of claim 1, wherein: the video-content object
comprises frames and audio and is associated with text; and the
object in the video-content object is one of: one or more of the
frames; one or more portions of the audio; or at least some of the
text.
6. The method of claim 1, wherein: the first recognition module is
a video-recognition module; the first feature vector represents an
intermediate output prediction; and the second recognition module
is an audio-recognition module.
7. The method of claim 1, further comprising: by one or more
computing devices, determining a third feature vector representing
the video-content object using a third recognition module of a
third type based on at least one of the first feature vector and
the second feature vector, wherein the third type is different from
the first and second types; and by one or more computing devices,
determining a context of the video-content object based on the
third feature vector.
8. The method of claim 1, wherein determining the first feature
vector comprises: extracting at least one feature from each frame
of a first set of frames of the video-content object to generate a
first set of feature vectors; and polling two or more of the first
set of feature vectors to generate the first feature vector.
9. One or more computer-readable non-transitory storage media
embodying software that is operable when executed to: access a
video-content object; determine a first feature vector representing
the video-content object using a first recognition module of a
first type based on an object in the video-content object;
determine a second feature vector representing the video-content
object using a second recognition module of a second type based on
the first feature vector, wherein the first type is different from
the second type; and determine a context of the video-content
object based on the second feature vector.
10. The media of claim 9, wherein: the first recognition module is
an audio-recognition module; the first feature vector represents a
predicted transcript of the video-content object, wherein the
transcript comprises text; and the second recognition module is a
text-recognition module.
11. The media of claim 9, wherein: the first recognition module is
a video-recognition module and the second recognition module is a
text-recognition module; the first recognition module is a
video-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is a
text-recognition module and the second recognition module is a
video-recognition module; the first recognition module is a
text-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is an
audio-recognition module and the second recognition module is a
video-recognition module; or the first recognition module is an
audio-recognition module and the second recognition module is a
text-recognition module.
12. The media of claim 9, wherein: the video-content object
corresponds to a node in a social graph of a social-networking
system; the social graph comprises a plurality of nodes and edges
connecting the nodes; and the context of the video-content object
is determined based on social-graph information based at least in
part on one or more nodes or edges connected to the node
corresponding to the video-content object, in addition to the
second feature vector.
13. The media of claim 9, wherein: the video-content object
comprises frames and audio and is associated with text; and the
object in the video-content object is one of: one or more of the
frames; one or more portions of the audio; or at least some of the
text.
14. The media of claim 9, wherein: the first recognition module is
a video-recognition module; the first feature vector represents an
intermediate output prediction; and the second recognition module
is an audio-recognition module.
15. The media of claim 9, wherein the software is further operable
when executed to: determine a third feature vector representing the
video-content object using a third recognition module of a third
type based on at least one of the first feature vector and the
second feature vector, wherein the third type is different from the
first and second types; and determine a context of the
video-content object based on the third feature vector.
16. The media of claim 9, wherein the software is operable to
determine the first feature vector by: extracting at least one
feature from each frame of a first set of frames of the
video-content object to generate a first set of feature vectors;
and polling two or more of the first set of feature vectors to
generate the first feature vector.
17. A system comprising: one or more processors; and a memory
coupled to the processors and comprising instructions operable when
executed by the processors to cause the processors to: access a
video-content object; determine a first feature vector representing
the video-content object using a first recognition module of a
first type based on an object in the video-content object;
determine a second feature vector representing the video-content
object using a second recognition module of a second type based on
the first feature vector, wherein the first type is different from
the second type; and determine a context of the video-content
object based on the second feature vector.
18. The system of claim 17, wherein: the first recognition module
is an audio-recognition module; the first feature vector represents
a predicted transcript of the video-content object, wherein the
transcript comprises text; and the second recognition module is a
text-recognition module.
19. The system of claim 17, wherein: the first recognition module
is a video-recognition module and the second recognition module is
a text-recognition module; the first recognition module is a
video-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is a
text-recognition module and the second recognition module is a
video-recognition module; the first recognition module is a
text-recognition module and the second recognition module is an
audio-recognition module; the first recognition module is an
audio-recognition module and the second recognition module is a
video-recognition module; or the first recognition module is an
audio-recognition module and the second recognition module is a
text-recognition module.
20. The method of claim 17, wherein: the video-content object
corresponds to a node in a social graph of a social-networking
system; the social graph comprises a plurality of nodes and edges
connecting the nodes; and the context of the video-content object
is determined based on social-graph information based at least in
part on one or more nodes or edges connected to the node
corresponding to the video-content object, in addition to the
second feature vector.
Description
PRIORITY
[0001] This application is a continuation under 35 U.S.C. .sctn.
120 of U.S. patent application Ser. No. 15/395,511, filed 30 Dec.
2016.
TECHNICAL FIELD
[0002] This disclosure generally relates to computer vision.
BACKGROUND
[0003] Computer vision is a computational process (or set of
computational processes) that facilitates machine understanding of
the content of an image or set of images, such as a video. For
example, computer vision may involve automatically extracting
features from an image, analyzing them, and generating an explicit
description or categorization of the image. Applications of
computer vision include controlling processes and systems,
navigation, event detection, organizing information, modeling
objects or environments, and automatic inspection.
[0004] A social-networking system, which may include a
social-networking website, may enable its users (such as persons or
organizations) to interact with it and with each other through it.
The social-networking system may, with input from a user, create
and store in the social-networking system a user profile associated
with the user. The user profile may include demographic
information, communication-channel information, and information on
personal interests of the user. The social-networking system may
also, with input from a user, create and store a record of
relationships of the user with other users of the social-networking
system, as well as provide services (e.g., wall posts,
photo-sharing, event organization, messaging, games, or
advertisements) to facilitate social interaction between or among
users.
SUMMARY OF PARTICULAR EMBODIMENTS
[0005] In particular embodiments, a video understanding platform
may be trained by machine learning to make a prediction about a
video-content object based on one or more of: frames of the
video-content object, audio of the video-content object, and text
associated with the video-content object. In particular
embodiments, a video understanding platform may comprise a
video-recognition model, an audio-recognition model, and a
text-recognition model. A video-recognition model may be trained by
machine learning to make a prediction about a video-content object
based on an analysis of one or more frames (e.g., a still image) of
the video-content object. An audio-recognition model may be trained
by machine learning to make a prediction about a video-content
object based on an analysis of part or all of the audio of a
video-content object (e.g., speech identification, language
identification, sound identification, source separation, etc.). A
text-recognition module may be trained by machine learning to make
a prediction about a video-content object based on text associated
with the video-content object (e.g., posts or comments associated
with a video-content object posted on an online social network,
text metadata associated with the video-content object, topic
classification information associated with the video-content
object, intent understanding information associated with the
video-content object, etc.). In particular embodiments, a
prediction about a video-content object may comprise a context, a
predicted future action, a predicted object, a predicted motion, or
any other suitable prediction. A context of a video-content object
may be one or more n-grams that describe the video-content object
or an aspect of the video-content object (e.g., a description of
objects or actions depicted, a category of the video-content
object, etc.). In particular embodiments, a computer-vision
platform may update a prediction about a video-content object based
on information not used to make a prior prediction (e.g.,
information received after the prior prediction was made). As an
example and not by way of limitation, a video-content object may be
a video that is streamed live and information (e.g., likes,
comments, shares, video content, etc.) may be received in an
ongoing manner and the computer-vision platform may update a
prediction based on this information. Although this disclosure may
describe a particular video understanding platform, this disclosure
contemplates any suitable video understanding platform.
[0006] The embodiments disclosed herein are only examples, and the
scope of this disclosure is not limited to them. Particular
embodiments may include all, some, or none of the components,
elements, features, functions, operations, or steps of the
embodiments disclosed above. Embodiments according to the invention
are in particular disclosed in the attached claims directed to a
method, a storage medium, a system and a computer program product,
wherein any feature mentioned in one claim category, e.g. method,
can be claimed in another claim category, e.g. system, as well. The
dependencies or references back in the attached claims are chosen
for formal reasons only. However any subject matter resulting from
a deliberate reference back to any previous claims (in particular
multiple dependencies) can be claimed as well, so that any
combination of claims and the features thereof are disclosed and
can be claimed regardless of the dependencies chosen in the
attached claims. The subject-matter which can be claimed comprises
not only the combinations of features as set out in the attached
claims but also any other combination of features in the claims,
wherein each feature mentioned in the claims can be combined with
any other feature or combination of other features in the claims.
Furthermore, any of the embodiments and features described or
depicted herein can be claimed in a separate claim and/or in any
combination with any embodiment or feature described or depicted
herein or with any of the features of the attached claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 illustrates an example network environment associated
with a social-networking system.
[0008] FIG. 2 illustrates an example social graph.
[0009] FIG. 3 illustrates an example view of a vector space.
[0010] FIG. 4 illustrates an example video understanding
engine.
[0011] FIG. 5 illustrates an example method for determining a
context of a video-content object.
[0012] FIG. 6 illustrates an example computer system.
DESCRIPTION OF EXAMPLE EMBODIMENTS
[0013] FIG. 1 illustrates an example network environment 100
associated with a social-networking system. Network environment 100
includes a user 101, a client system 130, a social-networking
system 160, and a third-party system 170 connected to each other by
a network 110. Although FIG. 1 illustrates a particular arrangement
of user 101, client system 130, social-networking system 160,
third-party system 170, and network 110, this disclosure
contemplates any suitable arrangement of user 101, client system
130, social-networking system 160, third-party system 170, and
network 110. As an example and not by way of limitation, two or
more of client system 130, social-networking system 160, and
third-party system 170 may be connected to each other directly,
bypassing network 110. As another example, two or more of client
system 130, social-networking system 160, and third-party system
170 may be physically or logically co-located with each other in
whole or in part. Moreover, although FIG. 1 illustrates a
particular number of users 101, client systems 130,
social-networking systems 160, third-party systems 170, and
networks 110, this disclosure contemplates any suitable number of
users 101, client systems 130, social-networking systems 160,
third-party systems 170, and networks 110. As an example and not by
way of limitation, network environment 100 may include multiple
users 101, client system 130, social-networking systems 160,
third-party systems 170, and networks 110.
[0014] In particular embodiments, user 101 may be an individual
(human user), an entity (e.g., an enterprise, business, or
third-party application), or a group (e.g., of individuals or
entities) that interacts or communicates with or over
social-networking system 160. In particular embodiments,
social-networking system 160 may be a network-addressable computing
system hosting an online social network. Social-networking system
160 may generate, store, receive, and send social-networking data,
such as, for example, user-profile data, concept-profile data,
social-graph information, or other suitable data related to the
online social network. Social-networking system 160 may be accessed
by the other components of network environment 100 either directly
or via network 110. In particular embodiments, social-networking
system 160 may include an authorization server (or other suitable
component(s)) that allows users 101 to opt in to or opt out of
having their actions logged by social-networking system 160 or
shared with other systems (e.g., third-party systems 170), for
example, by setting appropriate privacy settings. A privacy setting
of a user may determine what information associated with the user
may be logged, how information associated with the user may be
logged, when information associated with the user may be logged,
who may log information associated with the user, whom information
associated with the user may be shared with, and for what purposes
information associated with the user may be logged or shared.
Authorization servers may be used to enforce one or more privacy
settings of the users of social-networking system 30 through
blocking, data hashing, anonymization, or other suitable techniques
as appropriate. Third-party system 170 may be accessed by the other
components of network environment 100 either directly or via
network 110. In particular embodiments, one or more users 101 may
use one or more client systems 130 to access, send data to, and
receive data from social-networking system 160 or third-party
system 170. Client system 130 may access social-networking system
160 or third-party system 170 directly, via network 110, or via a
third-party system. As an example and not by way of limitation,
client system 130 may access third-party system 170 via
social-networking system 160. Client system 130 may be any suitable
computing device, such as, for example, a personal computer, a
laptop computer, a cellular telephone, a smartphone, a tablet
computer, or an augmented/virtual reality device.
[0015] This disclosure contemplates any suitable network 110. As an
example and not by way of limitation, one or more portions of
network 110 may include an ad hoc network, an intranet, an
extranet, a virtual private network (VPN), a local area network
(LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless
WAN (WWAN), a metropolitan area network (MAN), a portion of the
Internet, a portion of the Public Switched Telephone Network
(PSTN), a cellular telephone network, or a combination of two or
more of these. Network 110 may include one or more networks
110.
[0016] Links 150 may connect client system 130, social-networking
system 160, and third-party system 170 to communication network 110
or to each other. This disclosure contemplates any suitable links
150. In particular embodiments, one or more links 150 include one
or more wireline (such as for example Digital Subscriber Line (DSL)
or Data Over Cable Service Interface Specification (DOCSIS)),
wireless (such as for example Wi-Fi or Worldwide Interoperability
for Microwave Access (WiMAX)), or optical (such as for example
Synchronous Optical Network (SONET) or Synchronous Digital
Hierarchy (SDH)) links. In particular embodiments, one or more
links 150 each include an ad hoc network, an intranet, an extranet,
a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the
Internet, a portion of the PSTN, a cellular technology-based
network, a satellite communications technology-based network,
another link 150, or a combination of two or more such links 150.
Links 150 need not necessarily be the same throughout network
environment 100. One or more first links 150 may differ in one or
more respects from one or more second links 150.
[0017] FIG. 2 illustrates example social graph 200. In particular
embodiments, social-networking system 160 may store one or more
social graphs 200 in one or more data stores. In particular
embodiments, social graph 200 may include multiple nodes--which may
include multiple user nodes 202 or multiple concept nodes 204--and
multiple edges 206 connecting the nodes. Example social graph 200
illustrated in FIG. 2 is shown, for didactic purposes, in a
two-dimensional visual map representation. In particular
embodiments, a social-networking system 160, client system 130, or
third-party system 170 may access social graph 200 and related
social-graph information for suitable applications. The nodes and
edges of social graph 200 may be stored as data objects, for
example, in a data store (such as a social-graph database). Such a
data store may include one or more searchable or queryable indexes
of nodes or edges of social graph 200.
[0018] In particular embodiments, a user node 202 may correspond to
a user of social-networking system 160. As an example and not by
way of limitation, a user may be an individual (human user), an
entity (e.g., an enterprise, business, or third-party application),
or a group (e.g., of individuals or entities) that interacts or
communicates with or over social-networking system 160. In
particular embodiments, when a user registers for an account with
social-networking system 160, social-networking system 160 may
create a user node 202 corresponding to the user, and store the
user node 202 in one or more data stores. Users and user nodes 202
described herein may, where appropriate, refer to registered users
and user nodes 202 associated with registered users. In addition or
as an alternative, users and user nodes 202 described herein may,
where appropriate, refer to users that have not registered with
social-networking system 160. In particular embodiments, a user
node 202 may be associated with information provided by a user or
information gathered by various systems, including
social-networking system 160. As an example and not by way of
limitation, a user may provide his or her name, profile picture,
contact information, birth date, sex, marital status, family
status, employment, education background, preferences, interests,
or other demographic information. In particular embodiments, a user
node 202 may be associated with one or more data objects
corresponding to information associated with a user. In particular
embodiments, a user node 202 may correspond to one or more
webpages.
[0019] In particular embodiments, a concept node 204 may correspond
to a concept. As an example and not by way of limitation, a concept
may correspond to a place (such as, for example, a movie theater,
restaurant, landmark, or city); a website (such as, for example, a
website associated with social-network system 160 or a third-party
website associated with a web-application server); an entity (such
as, for example, a person, business, group, sports team, or
celebrity); a resource (such as, for example, an audio file, video
file, digital photo, text file, structured document, or
application) which may be located within social-networking system
160 or on an external server, such as a web-application server;
real or intellectual property (such as, for example, a sculpture,
painting, movie, game, song, idea, photograph, or written work); a
game; an activity; an idea or theory; an object in a
augmented/virtual reality environment; another suitable concept; or
two or more such concepts. A concept node 204 may be associated
with information of a concept provided by a user or information
gathered by various systems, including social-networking system
160. As an example and not by way of limitation, information of a
concept may include a name or a title; one or more images (e.g., an
image of the cover page of a book); a location (e.g., an address or
a geographical location); a website (which may be associated with a
URL); contact information (e.g., a phone number or an email
address); other suitable concept information; or any suitable
combination of such information. In particular embodiments, a
concept node 204 may be associated with one or more data objects
corresponding to information associated with concept node 204. In
particular embodiments, a concept node 204 may correspond to one or
more webpages.
[0020] In particular embodiments, a node in social graph 200 may
represent or be represented by a webpage (which may be referred to
as a "profile page"). Profile pages may be hosted by or accessible
to social-networking system 160. Profile pages may also be hosted
on third-party websites associated with a third-party system 170.
As an example and not by way of limitation, a profile page
corresponding to a particular external webpage may be the
particular external webpage and the profile page may correspond to
a particular concept node 204. Profile pages may be viewable by all
or a selected subset of other users. As an example and not by way
of limitation, a user node 202 may have a corresponding
user-profile page in which the corresponding user may add content,
make declarations, or otherwise express himself or herself. As
another example and not by way of limitation, a concept node 204
may have a corresponding concept-profile page in which one or more
users may add content, make declarations, or express themselves,
particularly in relation to the concept corresponding to concept
node 204.
[0021] In particular embodiments, a concept node 204 may represent
a third-party webpage or resource hosted by a third-party system
170. The third-party webpage or resource may include, among other
elements, content, a selectable or other icon, or other
inter-actable object (which may be implemented, for example, in
JavaScript, AJAX, or PHP codes) representing an action or activity.
As an example and not by way of limitation, a third-party webpage
may include a selectable icon such as "like," "check-in," "eat,"
"recommend," or another suitable action or activity. A user viewing
the third-party webpage may perform an action by selecting one of
the icons (e.g., "check-in"), causing a client system 130 to send
to social-networking system 160 a message indicating the user's
action. In response to the message, social-networking system 160
may create an edge (e.g., a check-in-type edge) between a user node
202 corresponding to the user and a concept node 204 corresponding
to the third-party webpage or resource and store edge 206 in one or
more data stores.
[0022] In particular embodiments, a pair of nodes in social graph
200 may be connected to each other by one or more edges 206. An
edge 206 connecting a pair of nodes may represent a relationship
between the pair of nodes. In particular embodiments, an edge 206
may include or represent one or more data objects or attributes
corresponding to the relationship between a pair of nodes. As an
example and not by way of limitation, a first user may indicate
that a second user is a "friend" of the first user. In response to
this indication, social-networking system 160 may send a "friend
request" to the second user. If the second user confirms the
"friend request," social-networking system 160 may create an edge
206 connecting the first user's user node 202 to the second user's
user node 202 in social graph 200 and store edge 206 as
social-graph information in one or more of data stores 164. In the
example of FIG. 2, social graph 200 includes an edge 206 indicating
a friend relation between user nodes 202 of user "A" and user "B"
and an edge indicating a friend relation between user nodes 202 of
user "C" and user "B." Although this disclosure describes or
illustrates particular edges 206 with particular attributes
connecting particular user nodes 202, this disclosure contemplates
any suitable edges 206 with any suitable attributes connecting user
nodes 202. As an example and not by way of limitation, an edge 206
may represent a friendship, family relationship, business or
employment relationship, fan relationship (including, e.g., liking,
etc.), follower relationship, visitor relationship (including,
e.g., accessing, viewing, checking-in, sharing, etc.), subscriber
relationship, superior/subordinate relationship, reciprocal
relationship, non-reciprocal relationship, another suitable type of
relationship, or two or more such relationships. Moreover, although
this disclosure generally describes nodes as being connected, this
disclosure also describes users or concepts as being connected.
Herein, references to users or concepts being connected may, where
appropriate, refer to the nodes corresponding to those users or
concepts being connected in social graph 200 by one or more edges
206.
[0023] In particular embodiments, an edge 206 between a user node
202 and a concept node 204 may represent a particular action or
activity performed by a user associated with user node 202 toward a
concept associated with a concept node 204. As an example and not
by way of limitation, as illustrated in FIG. 2, a user may "like,"
"attended," "played," "listened," "cooked," "worked at," or
"watched" a concept, each of which may correspond to an edge type
or subtype. A concept-profile page corresponding to a concept node
204 may include, for example, a selectable "check in" icon (such
as, for example, a clickable "check in" icon) or a selectable "add
to favorites" icon. Similarly, after a user clicks these icons,
social-networking system 160 may create a "favorite" edge or a
"check in" edge in response to a user's action corresponding to a
respective action. As another example and not by way of limitation,
a user (user "C") may listen to a particular song ("Imagine") using
a particular application (SPOTIFY, which is an online music
application). In this case, social-networking system 160 may create
a "listened" edge 206 and a "used" edge (as illustrated in FIG. 2)
between user nodes 202 corresponding to the user and concept nodes
204 corresponding to the song and application to indicate that the
user listened to the song and used the application. Moreover,
social-networking system 160 may create a "played" edge 206 (as
illustrated in FIG. 2) between concept nodes 204 corresponding to
the song and the application to indicate that the particular song
was played by the particular application. In this case, "played"
edge 206 corresponds to an action performed by an external
application (SPOTIFY) on an external audio file (the song
"Imagine"). Although this disclosure describes particular edges 206
with particular attributes connecting user nodes 202 and concept
nodes 204, this disclosure contemplates any suitable edges 206 with
any suitable attributes connecting user nodes 202 and concept nodes
204. Moreover, although this disclosure describes edges between a
user node 202 and a concept node 204 representing a single
relationship, this disclosure contemplates edges between a user
node 202 and a concept node 204 representing one or more
relationships. As an example and not by way of limitation, an edge
206 may represent both that a user likes and has used at a
particular concept. Alternatively, another edge 206 may represent
each type of relationship (or multiples of a single relationship)
between a user node 202 and a concept node 204 (as illustrated in
FIG. 2 between user node 202 for user "E" and concept node 204 for
"SPOTIFY").
[0024] In particular embodiments, social-networking system 160 may
create an edge 206 between a user node 202 and a concept node 204
in social graph 200. As an example and not by way of limitation, a
user viewing a concept-profile page (such as, for example, by using
a web browser or a special-purpose application hosted by the user's
client system 130) may indicate that he or she likes the concept
represented by the concept node 204 by clicking or selecting a
"Like" icon, which may cause the user's client system 130 to send
to social-networking system 160 a message indicating the user's
liking of the concept associated with the concept-profile page. In
response to the message, social-networking system 160 may create an
edge 206 between user node 202 associated with the user and concept
node 204, as illustrated by "like" edge 206 between the user and
concept node 204. In particular embodiments, social-networking
system 160 may store an edge 206 in one or more data stores. In
particular embodiments, an edge 206 may be automatically formed by
social-networking system 160 in response to a particular user
action. As an example and not by way of limitation, if a first user
uploads a picture, watches a movie, or listens to a song, an edge
206 may be formed between user node 202 corresponding to the first
user and concept nodes 204 corresponding to those concepts.
Although this disclosure describes forming particular edges 206 in
particular manners, this disclosure contemplates forming any
suitable edges 206 in any suitable manner.
[0025] FIG. 3 illustrates an example view of a vector space 300.
Vector space 300 may also be referred to as a feature space or an
embedding space. In particular embodiments, an object or an n-gram
may be represented in a d-dimensional vector space, where d denotes
any suitable number of dimensions. An object may represent data,
such as audio data or video data. Although the vector space 300 is
illustrated as a three-dimensional space, this is for illustrative
purposes only, as the vector space 300 may be of any suitable
dimension. In particular embodiments, an object may be represented
in the vector space 300 as a feature vector. A feature vector may
also be referred to as an embedding. Each vector may comprise
coordinates corresponding to a particular point in the vector space
300 (i.e., the terminal point of the vector). As an example and not
by way of limitation, feature vectors 310, 320, and 330 may be
represented as points in the vector space 300, as illustrated in
FIG. 3. An object may be mapped to a respective vector
representation. As an example and not by way of limitation, objects
t.sub.1 and t.sub.2 may be mapped to feature vectors and in the
vector space 300, respectively, by applying a function . The
function may map objects to feature vectors by feature extraction,
which may start from an initial set of measured data and build
derived values (e.g., features). When an object has data that is
either too large to be efficiently processed or comprises redundant
data, may map the object to a feature vector using a transformed
reduced set of features (e.g., feature selection). A feature vector
may comprise information related to the object. In particular
embodiments, an object may be mapped to a feature vector based on
one or more properties, attributes, or features of the object,
relationships of the object with other objects, or any other
suitable information associated with the object. As an example and
not by way of limitation, an object comprising a video or an image
may be mapped to a vector representation in the vector space 300 by
using an algorithm to detect or isolate various desired portions or
shapes of the object. Features of the feature vector may be based
on information obtained from edge detection, corner detection, blob
detection, ridge detection, scale-invariant feature transformation,
edge direction, changing intensity, autocorrelation, motion
detection, optical flow, thresholding, blob extraction, template
matching, Hough transformation (e.g., lines, circles, ellipses,
arbitrary shapes), or any other suitable information. As another
example and not by way of limitation, an object comprising audio
data may be mapped to a feature vector based on features such as a
spectral slope, a tonality coefficient, an audio spectrum centroid,
an audio spectrum envelope, a Mel-frequency cepstrum, or any other
suitable information. In particular embodiments, an n-gram may be
mapped to a feature vector by a dictionary trained to map text to a
feature vector. As an example and not by way of limitation, a
model, such as Word2vec, may be used to map an n-gram to a feature
vector. In particular embodiments, feature vectors or embeddings
may be robust to basic changes like text addition or changes to
aspect ratio. In particular embodiments, social-networking system
160 may map objects of different modalities (e.g., visual, audio,
text) to a particular vector space or using a separate function. In
particular embodiments, social-networking system 160 may map
objects of different modalities to the same vector space or use a
function jointly trained to map one or more modalities to a feature
vector (e.g., between visual, audio, text). Although this
disclosure describes representing a video-content object in a
vector space in a particular manner, this disclosure contemplates
representing a video-content object in a vector space in any
suitable manner.
[0026] In particular embodiments, social-networking system 160 may
calculate a similarity metric of feature vectors in vector space
300. A similarity metric may be a cosine similarity, a Minkowski
distance, a Mahalanobis distance, a Jaccard similarity coefficient,
or any other suitable similarity metric. As an example and not by
way of limitation, a similarity metric of and may be a cosine
similarity
v 1 v 2 .fwdarw. v 1 v 2 .fwdarw. . ##EQU00001##
As another example and not by way of limitation, a similarity
metric of and may be a Euclidean distance .parallel.-.parallel.. A
similarity metric of two feature vectors may represent how similar
the two objects corresponding to the two feature vectors,
respectively, are to one another, as measured by the distance
between the two feature vectors in the vector space 300. As an
example and not by way of limitation, feature vector 310 and
feature vector 320 may correspond to video-content objects that are
more similar to one another than the video-content objects
corresponding to feature vector 310 and feature vector 330, based
on the distance between the respective feature vectors. In
particular embodiments, social-networking system 160 may determine
a cluster of vector space 300. A cluster may be a set of one or
more points corresponding to feature vectors of objects or n-grams
in vector space 300, and the objects or n-grams whose feature
vectors are in the cluster may belong to the same class or have
some semantic relationship to one another. As an example and not by
way of limitation, a cluster may correspond to sports-related
content and another cluster may correspond to food-related content.
Although this disclosure describes calculating similarity metrics
in a particular manner, this disclosure contemplates calculating
similarity metrics in any suitable manner.
[0027] More information on vector spaces, embeddings, feature
vectors, and similarity metrics may be found in U.S. patent
application Ser. No. 14/949,436, filed 23 Nov. 2015, U.S. patent
application Ser. No. 14/981,413, filed 28 Dec. 2015, U.S. patent
application Ser. No. 15/286,315, filed 5 Oct. 2016, and U.S. patent
application Ser. No. 15/365,789, filed 30 Nov. 2016, each of which
is incorporated by reference.
[0028] In particular embodiments, a video understanding platform
may be trained by machine learning to make a prediction about a
video-content object based on one or more of: frames of the
video-content object, audio of the video-content object, and text
associated with the video-content object. In particular
embodiments, a video understanding platform may comprise a
video-recognition model, an audio-recognition model, and a
text-recognition model. A video-recognition model may be trained by
machine learning to make a prediction about a video-content object
based on an analysis of one or more frames (e.g., a still image) of
the video-content object. An audio-recognition model may be trained
by machine learning to make a prediction about a video-content
object based on an analysis of part or all of the audio of a
video-content object (e.g., speech identification, language
identification, sound identification, source separation, etc.). A
text-recognition module may be trained by machine learning to make
a prediction about a video-content object based on text associated
with the video-content object (e.g., posts or comments associated
with a video-content object posted on an online social network,
text metadata associated with the video-content object, topic
classification information associated with the video-content
object, intent understanding information associated with the
video-content object, etc.). In particular embodiments, a
prediction about a video-content object may comprise a context, a
predicted future action, a predicted object, a predicted motion, or
any other suitable prediction. A context of a video-content object
may be one or more n-grams that describe the video-content object
or an aspect of the video-content object (e.g., a description of
objects or actions depicted, a category of the video-content
object, etc.). In particular embodiments, a computer-vision
platform may update a prediction about a video-content object based
on information not used to make a prior prediction (e.g.,
information received after the prior prediction was made). As an
example and not by way of limitation, a video-content object may be
a video that is streamed live and information (e.g., likes,
comments, shares, video content, etc.) may be received in an
ongoing manner and the computer-vision platform may update a
prediction based on this information. Although this disclosure may
describe a particular video understanding platform, this disclosure
contemplates any suitable video understanding platform.
[0029] FIG. 4 illustrates an example video understanding engine
400. In particular embodiments, video understanding engine 400 may
comprise a video-recognition module 410, a text-recognition module
420, and an audio-recognition module 430. In particular
embodiments, video-recognition module 410 may be trained by machine
learning to receive a feature vector representing a video-content
object based on one or more frames of the video-content object and
output a prediction about the video-content object. In particular
embodiments, text-recognition module 420 may be trained by machine
learning to receive a feature vector representing a video-content
object based on text associated with the video-content object and
output a prediction about the video-content object. In particular
embodiments, audio-recognition module 430 may be trained by machine
learning to receive a feature vector representing a video-content
object based on one or more portions of audio of the video-content
object and output a prediction about the video-content object.
Although this disclosure may describe a particular video
understanding engine, this disclosure contemplates any suitable
video understanding engine.
[0030] In particular embodiments, a video-content object may
comprise frames and audio. As an example and not by way of
limitation, the video-content object may be a video file (e.g.,
MP4, WMV, AVI, etc.) comprising video data in a video format (e.g.,
VP9, HEVC/H.265, etc.) and audio data in an audio format (e.g.,
MP3, AAC, Vorbis, FLAC, Opus, etc.). In particular embodiments, the
video-content object may be associated with text. As an example and
not by way of limitation, the video-content object may be
associated with metadata. The metadata may include information
about the production of the video-content object (e.g., the date,
location, or author of the video), descriptive information about
the video-content object (e.g., a summary of the video-content
object, identities of people depicted, background information about
an event depicted, why the video-content object was created, etc.),
information about the content type (e.g., news report, birthday
party, live stream, etc.), keywords associated with the
video-content object, technical information about the video-content
object (e.g., format, file size, duration, format, etc.), a
transcript of the video-content object, or any other suitable
metadata. As another example and not by way of limitation, the
video-content object may be posted on an online social network and
have associated text such as a post or a comment. Although this
disclosure may describe a particular video-content object, this
disclosure contemplates any suitable video-content object.
[0031] In particular embodiments, social-networking system 160 may
access a feature vector representing the video-content object based
on one or more frames of the video-content object. As an example
and not by way of limitation, the feature vector may be determined
based on feature extraction of features of the one or more frames.
As another example and not by way of limitation, the feature vector
of the video-content object may be based on one or more feature
vectors of the one or more frames (e.g., pooling the feature
vectors). In particular embodiments, the video-content object may
correspond to a node in a social graph of the social-networking
system 160. In particular embodiments, the video-content object may
be stored in a data store (e.g., a social-graph database) and
social-networking system 160 may access the feature vector from the
data store. In particular embodiments, the social-networking system
160 may access the feature vector by accessing the video-content
object and mapping the video-content object to the feature vector.
Although this disclosure may describe accessing a feature vector in
a particular manner, this disclosure contemplates accessing a
feature vector in any suitable particular manner.
[0032] In particular embodiments, social-networking system 160 may
access a feature vector representing the video-content based on at
least some of the text associated with the video-content object. In
particular embodiments, the text associated with the video-content
object may be a transcript of one or more portions of the audio,
metadata associated with the video-content object, or a post by a
user of the social-networking system associated with the
video-content object. As an example and not by way of limitation,
the video-content object may be posted on social-networking system
160, and social-networking system 160 may access a feature vector
based on a comment associated with the video-content object posted
on the online social network. As another example and not by way of
limitation, social-networking system 160 may access a feature
vector based on metadata associated with the video-content object
that indicates that the video-content object was created by a
particular user. In particular embodiments, a feature vector may be
based on topic classification information associated with the
video-content object. As an example and not by way of limitation, a
video-content object may have an associated topic comprising the
text "opera" and a feature vector may be based on the text "opera."
In particular embodiments, social-networking system 160 may train a
language module (e.g., by machine learning) based on the text. A
feature vector may be based on the output of a language module.
Although this disclosure may describe accessing a feature vector in
a particular manner, this disclosure contemplates accessing a
feature vector in any suitable particular manner.
[0033] In particular embodiments, social-networking system 160 may
access a feature vector representing the video-content object based
on one or more portions of the audio. As an example and not by way
of limitation, the video-content object may comprise a video of a
birthday party and the feature vector may be based on a portion of
audio where people sing "Happy Birthday to You." As another example
and not by way of limitation, the video-content object may comprise
a video of a blackbird and a feature vector may be based on a
portion of audio where the blackbird vocalizes (i.e., its bird
song). In particular embodiments, a feature vector may be based on
audio analysis. As an example and not by way of limitation, the
feature vector may be based on identification of speech, an
identified language of speech, an identified sound (e.g., a dog
barking), source separation (e.g., an identified number of
speakers, separating different sound sources into separate audio
tracks, etc.), or any other suitable information based on audio
analysis. Although this disclosure may describe accessing a feature
vector in a particular manner, this disclosure contemplates
accessing a feature vector in any suitable particular manner.
[0034] In particular embodiments, input to fusion module 440 may
comprise one or more predictions made by video-recognition module
410, text-recognition module 420, or audio-recognition module 430.
As an example and not by way of limitation, a video-content object
may depict a boxing match. Video-recognition module 410 may predict
that the video-content object depicts boxing based on one or more
frames of the video-content object (e.g., by extracting features,
such as images of boxers wearing boxing gloves, the boxing ring,
the referee, etc.). Text-recognition module 420 may predict that
the video-content object depicts a fight based on text associated
with the video-content object, such as "fight," "punch," or
"knockout." Audio-recognition module 430 may predict that the
video-content object depicts a sporting event based on a portion of
the audio, such as audio of the crowd cheering or commentary
provided by sportscasters. Each of these predictions may be used as
an input to fusion module 440. Fusion module 440 may output a
prediction (e.g., that the video-content object depicts a boxing
match) or a feature vector representing the video-content object.
Although this disclosure may describe determining a feature vector
in a particular manner, this disclosure contemplates determining a
feature vector in any suitable particular manner.
[0035] In particular embodiments, one of video-recognition module
410, text-recognition module 420, and audio-recognition module 430
may generate a feature vector based on one or more outputs of
another one of video-recognition module 410, text-recognition
module 420, and audio-recognition module 430. As an example and not
by way of limitation, audio-recognition module 430 may output a
predicted transcript of a video-content object. This predicted
transcript may comprise text and be used as an input to
text-recognition module 420. As another example and not by way of
limitation, video-recognition module 410 may generate an
intermediate output prediction and the intermediate output
prediction may be used as an input to audio-recognition module 430.
Although this disclosure may describe particular inputs and
outputs, this disclosure contemplates any suitable inputs and
outputs.
[0036] In particular embodiments, fusion module 440 may be trained
to take as inputs one or more of the frames of a video-content
object, text associated with the video-content object, and one or
more portions of the audio of the video-content object and output a
feature vector representing the video-content object based on a
combination the inputs. Video understanding engine 440 may comprise
a fusion module 440, but not video-recognition module 410,
text-recognition module 420, or audio-recognition module 430.
Additionally or alternatively, fusion module 440 may output a
prediction about the video-content object. In particular
embodiments, video understanding engine 400 may comprise one or
more of video-recognition module 410, text-recognition module 420,
or audio-recognition module 430, fusion module 440, configured in
any suitable manner. Although this disclosure may describe a
particular video understanding engine, this disclosure contemplates
any suitable video understanding engine.
[0037] In particular embodiments, video understanding engine 400
may comprise a fusion module 440. Fusion module 440 may be trained
by machine learning to make a prediction about the video-content
object based on one or more frames of the video-content object,
text associated with the video-content object, and one or more
portions of audio of the video-content object. In particular
embodiments, fusion module 440 may be trained by machine learning
to determine a feature vector representing the video-content object
based on a combination of a feature vector based on one or more
frames of the video-content object, a feature vector based on text
associated with the video-content object, and a feature vector
based on one or more portions of audio of the video-content object.
As an example and not by way of limitation, a video-content object
may depict a birthday party. A feature vector based on one or more
frames of the video-content object may be input to fusion module
440, which may be based on recognizing objects depicted in the
frames, such as a birthday cake or party hats. A feature vector
based on text associated with the video-content object, such as
posts on an online social network that include the text "Happy
Birthday," or the title for the video-content object "My Birthday
Party," may be input into fusion module 440. A feature vector based
on one or more portions of audio of the video-content object, such
as the audio of a group of people depicted in the video-content
object singing the Happy Birthday Song, may be input into fusion
module 440. Fusion module 440 may output a feature vector
representing the video-content object based on a combination of the
inputted feature vectors. Although this disclosure may describe
determining a feature vector in a particular manner, this
disclosure contemplates determining a feature vector in any
suitable particular manner.
[0038] In particular embodiments, fusion module 440 may determine a
context of the video-content object. Fusion module 440 may be
trained by machine learning, to determine the context based on a
feature vector representing the video-content object, the feature
vector being based on a combination of a feature vector based on
one or more frames of the video-content object, a feature vector
based on at least some of the text associated with the
video-content object, and a feature vector based on one or more
portions of audio of the video-content object. In particular
embodiments, fusion module 440 may determine a context of the
video-content object based on social-graph information based at
least in part on one or more nodes or edges connected to the node
corresponding to the video-content object. As an example and not by
way of limitation, a video-content object may be posted on a user's
page on an online social network. The user may have posted the
video on her birthday, as determined by the user profile of the
user. The context may be that the video-content object depicts the
user's birthday party, as determined by a feature vector feature
vector representing the video-content object and the social-graph
information. In particular embodiments, determining a context of a
video-content object may comprise recognizing a physical object
(e.g., a book), identifying a particular physical object (e.g., the
book "Oh, The Places You'll Go!" by Dr. Seuss), detecting a
physical object, tracking a physical object, recognizing a pose
(e.g., sitting, standing, etc.), recognizing a face, determining a
topic (e.g., sports, politics, documentary, etc.), recognizing a
scene (e.g., classroom, forest, etc.), recognizing an activity
(e.g., throwing a ball, jogging, etc.), recognizing behavior (e.g.,
laughing, crying, etc.), or recognizing any other information
associated with the video-content object. Although this disclosure
may describe determining a context of a video-content object in a
particular manner, this disclosure contemplates determining a
context of a video-content object in any suitable manner.
[0039] In particular embodiments, social-networking system 160 may
receive a request to access the video-content object from a client
device of a user of the social-networking system. In particular
embodiments, social-networking system 160 may generate a
recommendation for a second video-content object based on the
feature vector of the video-content object and a user profile for
the user. As an example and not by way of limitation, a user may
access a first video-content object depicting a review of a mobile
phone. Fusion module 440 may determine a feature vector
representing the first video-content object. Further, based on
social-graph information, social-networking system 160 may
determine that the user is age 23 and likes the company APPLE.
Social-networking system 160 may generate a recommendation for a
second video-content object that features the APPLE IPHONE based on
a similarity metric between the feature vector representing the
first video-content object and a feature vector representing the
second video-content object, and based on determining that users
between the ages of 18 and 25 tend to prefer APPLE IPHONEs to other
mobile phones. Social-networking system 160 may send, to the user's
client device, the recommendation for the second video-content
object. Although this disclosure may describe recommending a
video-content object in a particular manner, this disclosure
contemplates recommending a video-content object in any suitable
manner.
[0040] In particular embodiments, determining the context of the
video-content object may comprise determining that the
video-content object is inappropriate. As an example and not by way
of limitation, fusion module 440 may output a prediction that a
video-content object depicts nudity or sexual content, violent or
graphic content, hateful content (e.g., promotes or condones
violence against individuals or groups), fraudulent or misleading
content (e.g., a pyramid scheme), harmful or dangerous content
(e.g., encourages others to do harmful activities), threatening
material, or material that violates copyright law. In particular
embodiments, social-networking system 160 may remove a second
video-content object based on determining that the video-content
object and the second video-content object are similar based on the
feature vector for the video-content object and a feature vector
for the second video-content object. As an example and not by way
of limitation, fusion module 440 may determine that a video-content
object depicts material that depicts violent content.
Social-networking system 160 may determine that a second
video-content object is similar to the video-content object based
on a cosine similarity between a feature vector representing the
video-content object and a feature vector representing the second
video-content object. Based on determining that the second
video-content object is similar to the video-content object,
social-networking system 160 may remove the second video-content
object. Although this disclosure may describe determining that the
video-content object is inappropriate and removing a video-content
object in a particular manner, this disclosure contemplates
determining that the video-content object is inappropriate and
removing a video-content object in any suitable manner.
[0041] In particular embodiments, social-networking system 160 may
receive, a query associated with the video-content object from a
client device of a user of the social-networking system. The user
may submit the query to the social-networking system 160 by, for
example, selecting a query input or inputting text into query
field. A user of an online social network may search for
information relating to a specific subject matter (e.g., users,
concepts, external content or resource) by providing a short phrase
describing the subject matter, often referred to as a "search
query," to a search engine. The query may be an unstructured text
query and may comprise one or more text strings (which may include
one or more n-grams). In general, a user may input any character
string into a query field to search for content on the
social-networking system 160 that matches the text query. The query
may comprise a plurality of n-grams. As an example and not by way
of limitation, the querying user may have inputted the query "cats
afraid of cucumbers." Although this disclosure describes receiving
a query in a particular manner, this disclosure contemplates
receiving a query in any suitable manner.
[0042] In particular embodiments, social-networking system 160 may
identify one or more objects matching the query. Social-networking
system 160 may search a data store (or, in particular, a
social-graph database) to identify content matching the query. The
search engine may conduct a search based on the query phrase using
various search algorithms and generate search results that identify
resources or content (e.g., user-profile interfaces,
content-profile interfaces, or external resources) that are most
likely to be related to the search query. Although this disclosure
describes identifying objects matching a query in a particular
manner, this disclosure contemplates identifying objects matching a
query in any suitable manner.
[0043] In particular embodiments, social-networking system 160 may,
for each identified objects, access a feature vector representing
the identified object. Social-networking system 160 may map objects
to feature vectors by feature extraction, or access a cached
feature vector for an object that has been previously mapped. In
particular embodiments, social-networking system 160 may rank each
identified object based on a similarity metric between the feature
vector representing the video-content object and the feature vector
representing the identified object. As an example and not by way of
limitation, the similarity metric may be a cosine similarity
between the feature vector representing the video-content object
and the feature vector representing the identified object. The
objects may be ranked higher if the cosine similarity associated
with the object is larger. In particular embodiments,
social-networking system 160 may send, to the client system in
response to the query, one or more search results corresponding to
one or more of the identified objects, respectively, each
identified object corresponding to a search result having a rank
greater than a threshold rank. As an example and not by way of
limitation, a threshold rank may be a static number (e.g., 0.8). As
another example and not by way of limitation, a threshold rank may
be determined such that a particular number of search results are
sent to the user (e.g., the threshold rank may be determined such
that 50 search results corresponding to the top-ranked identified
objects have a rank greater than the threshold rank). Although this
disclosure describes ranking and sending objects in a particular
manner, this disclosure contemplates ranking and sending objects in
any suitable manner.
[0044] FIG. 5 illustrates an example method 500 for determining a
context of a video-content object. The method may begin at step
510, where social-networking system 160 may access a first feature
vector representing a video-content object corresponding to a node
in a social graph of a social-networking system, wherein: the
video-content object comprises frames and audio and is associated
with text, the first feature vector is based on one or more of the
frames of the video-content object, and the social graph comprises
a plurality of nodes and edges connecting the nodes. At step 520,
social-networking system 160 may access a second feature vector
representing the video-content object, wherein the second feature
vector is based on at least some of the text. At step 530,
social-networking system 160 may access a third feature vector
representing the video-content object, wherein the third feature
vector is based on one or more portions of the audio. At step 540,
social-networking system 160 may determine a fourth feature vector
representing the video-content object, wherein the fourth feature
vector is based on a combination of the first, second, and third
feature vectors. At step 550, social-networking system 160 may
determine a context of the video-content object based on the fourth
feature vector and social-graph information based at least in part
on one or more nodes or edges connected to the node corresponding
to the video-content object. Particular embodiments may repeat one
or more steps of the method of FIG. 5, where appropriate. Although
this disclosure describes and illustrates particular steps of the
method of FIG. 5 as occurring in a particular order, this
disclosure contemplates any suitable steps of the method of FIG. 5
occurring in any suitable order. Moreover, although this disclosure
describes and illustrates an example method for determining a
context of a video-content object including the particular steps of
the method of FIG. 5, this disclosure contemplates any suitable
method for determining a context of a video-content object
including any suitable steps, which may include all, some, or none
of the steps of the method of FIG. 5, where appropriate.
Furthermore, although this disclosure describes and illustrates
particular components, devices, or systems carrying out particular
steps of the method of FIG. 5, this disclosure contemplates any
suitable combination of any suitable components, devices, or
systems carrying out any suitable steps of the method of FIG.
5.
[0045] FIG. 6 illustrates an example computer system 600. In
particular embodiments, one or more computer systems 600 perform
one or more steps of one or more methods described or illustrated
herein. In particular embodiments, one or more computer systems 600
provide functionality described or illustrated herein. In
particular embodiments, software running on one or more computer
systems 600 performs one or more steps of one or more methods
described or illustrated herein or provides functionality described
or illustrated herein. Particular embodiments include one or more
portions of one or more computer systems 600. Herein, reference to
a computer system may encompass a computing device, and vice versa,
where appropriate. Moreover, reference to a computer system may
encompass one or more computer systems, where appropriate.
[0046] This disclosure contemplates any suitable number of computer
systems 600. This disclosure contemplates computer system 600
taking any suitable physical form. As example and not by way of
limitation, computer system 600 may be an embedded computer system,
a system-on-chip (SOC), a single-board computer system (SBC) (such
as, for example, a computer-on-module (COM) or system-on-module
(SOM)), a desktop computer system, a laptop or notebook computer
system, an interactive kiosk, a mainframe, a mesh of computer
systems, a mobile telephone, a personal digital assistant (PDA), a
server, a tablet computer system, an augmented/virtual reality
device, or a combination of two or more of these. Where
appropriate, computer system 600 may include one or more computer
systems 600; be unitary or distributed; span multiple locations;
span multiple machines; span multiple data centers; or reside in a
cloud, which may include one or more cloud components in one or
more networks. Where appropriate, one or more computer systems 600
may perform without substantial spatial or temporal limitation one
or more steps of one or more methods described or illustrated
herein. As an example and not by way of limitation, one or more
computer systems 600 may perform in real time or in batch mode one
or more steps of one or more methods described or illustrated
herein. One or more computer systems 600 may perform at different
times or at different locations one or more steps of one or more
methods described or illustrated herein, where appropriate.
[0047] In particular embodiments, computer system 600 includes a
processor 602, memory 604, storage 606, an input/output (I/O)
interface 608, a communication interface 610, and a bus 612.
Although this disclosure describes and illustrates a particular
computer system having a particular number of particular components
in a particular arrangement, this disclosure contemplates any
suitable computer system having any suitable number of any suitable
components in any suitable arrangement.
[0048] In particular embodiments, processor 602 includes hardware
for executing instructions, such as those making up a computer
program. As an example and not by way of limitation, to execute
instructions, processor 602 may retrieve (or fetch) the
instructions from an internal register, an internal cache, memory
604, or storage 606; decode and execute them; and then write one or
more results to an internal register, an internal cache, memory
604, or storage 606. In particular embodiments, processor 602 may
include one or more internal caches for data, instructions, or
addresses. This disclosure contemplates processor 602 including any
suitable number of any suitable internal caches, where appropriate.
As an example and not by way of limitation, processor 602 may
include one or more instruction caches, one or more data caches,
and one or more translation lookaside buffers (TLBs). Instructions
in the instruction caches may be copies of instructions in memory
604 or storage 606, and the instruction caches may speed up
retrieval of those instructions by processor 602. Data in the data
caches may be copies of data in memory 604 or storage 606 for
instructions executing at processor 602 to operate on; the results
of previous instructions executed at processor 602 for access by
subsequent instructions executing at processor 602 or for writing
to memory 604 or storage 606; or other suitable data. The data
caches may speed up read or write operations by processor 602. The
TLBs may speed up virtual-address translation for processor 602. In
particular embodiments, processor 602 may include one or more
internal registers for data, instructions, or addresses. This
disclosure contemplates processor 602 including any suitable number
of any suitable internal registers, where appropriate. Where
appropriate, processor 602 may include one or more arithmetic logic
units (ALUs); be a multi-core processor; or include one or more
processors 602. Although this disclosure describes and illustrates
a particular processor, this disclosure contemplates any suitable
processor.
[0049] In particular embodiments, memory 604 includes main memory
for storing instructions for processor 602 to execute or data for
processor 602 to operate on. As an example and not by way of
limitation, computer system 600 may load instructions from storage
606 or another source (such as, for example, another computer
system 600) to memory 604. Processor 602 may then load the
instructions from memory 604 to an internal register or internal
cache. To execute the instructions, processor 602 may retrieve the
instructions from the internal register or internal cache and
decode them. During or after execution of the instructions,
processor 602 may write one or more results (which may be
intermediate or final results) to the internal register or internal
cache. Processor 602 may then write one or more of those results to
memory 604. In particular embodiments, processor 602 executes only
instructions in one or more internal registers or internal caches
or in memory 604 (as opposed to storage 606 or elsewhere) and
operates only on data in one or more internal registers or internal
caches or in memory 604 (as opposed to storage 606 or elsewhere).
One or more memory buses (which may each include an address bus and
a data bus) may couple processor 602 to memory 604. Bus 612 may
include one or more memory buses, as described below. In particular
embodiments, one or more memory management units (MMUs) reside
between processor 602 and memory 604 and facilitate accesses to
memory 604 requested by processor 602. In particular embodiments,
memory 604 includes random access memory (RAM). This RAM may be
volatile memory, where appropriate Where appropriate, this RAM may
be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where
appropriate, this RAM may be single-ported or multi-ported RAM.
This disclosure contemplates any suitable RAM. Memory 604 may
include one or more memories 604, where appropriate. Although this
disclosure describes and illustrates particular memory, this
disclosure contemplates any suitable memory.
[0050] In particular embodiments, storage 606 includes mass storage
for data or instructions. As an example and not by way of
limitation, storage 606 may include a hard disk drive (HDD), a
floppy disk drive, flash memory, an optical disc, a magneto-optical
disc, magnetic tape, or a Universal Serial Bus (USB) drive or a
combination of two or more of these. Storage 606 may include
removable or non-removable (or fixed) media, where appropriate.
Storage 606 may be internal or external to computer system 600,
where appropriate. In particular embodiments, storage 606 is
non-volatile, solid-state memory. In particular embodiments,
storage 606 includes read-only memory (ROM). Where appropriate,
this ROM may be mask-programmed ROM, programmable ROM (PROM),
erasable PROM (EPROM), electrically erasable PROM (EEPROM),
electrically alterable ROM (EAROM), or flash memory or a
combination of two or more of these. This disclosure contemplates
mass storage 606 taking any suitable physical form. Storage 606 may
include one or more storage control units facilitating
communication between processor 602 and storage 606, where
appropriate. Where appropriate, storage 606 may include one or more
storages 606. Although this disclosure describes and illustrates
particular storage, this disclosure contemplates any suitable
storage.
[0051] In particular embodiments, I/O interface 608 includes
hardware, software, or both, providing one or more interfaces for
communication between computer system 600 and one or more I/O
devices. Computer system 600 may include one or more of these I/O
devices, where appropriate. One or more of these I/O devices may
enable communication between a person and computer system 600. As
an example and not by way of limitation, an I/O device may include
a keyboard, keypad, microphone, monitor, mouse, printer, scanner,
speaker, still camera, stylus, tablet, touch screen, trackball,
video camera, another suitable I/O device or a combination of two
or more of these. An I/O device may include one or more sensors.
This disclosure contemplates any suitable I/O devices and any
suitable I/O interfaces 608 for them. Where appropriate, I/O
interface 608 may include one or more device or software drivers
enabling processor 602 to drive one or more of these I/O devices.
I/O interface 608 may include one or more I/O interfaces 608, where
appropriate. Although this disclosure describes and illustrates a
particular I/O interface, this disclosure contemplates any suitable
I/O interface.
[0052] In particular embodiments, communication interface 610
includes hardware, software, or both providing one or more
interfaces for communication (such as, for example, packet-based
communication) between computer system 600 and one or more other
computer systems 600 or one or more networks. As an example and not
by way of limitation, communication interface 610 may include a
network interface controller (NIC) or network adapter for
communicating with an Ethernet or other wire-based network or a
wireless NIC (WNIC) or wireless adapter for communicating with a
wireless network, such as a WI-FI network. This disclosure
contemplates any suitable network and any suitable communication
interface 610 for it. As an example and not by way of limitation,
computer system 600 may communicate with an ad hoc network, a
personal area network (PAN), a local area network (LAN), a wide
area network (WAN), a metropolitan area network (MAN), or one or
more portions of the Internet or a combination of two or more of
these. One or more portions of one or more of these networks may be
wired or wireless. As an example, computer system 600 may
communicate with a wireless PAN (WPAN) (such as, for example, a
BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular
telephone network (such as, for example, a Global System for Mobile
Communications (GSM) network), or other suitable wireless network
or a combination of two or more of these. Computer system 600 may
include any suitable communication interface 610 for any of these
networks, where appropriate. Communication interface 610 may
include one or more communication interfaces 610, where
appropriate. Although this disclosure describes and illustrates a
particular communication interface, this disclosure contemplates
any suitable communication interface.
[0053] In particular embodiments, bus 612 includes hardware,
software, or both coupling components of computer system 600 to
each other. As an example and not by way of limitation, bus 612 may
include an Accelerated Graphics Port (AGP) or other graphics bus,
an Enhanced Industry Standard Architecture (EISA) bus, a front-side
bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard
Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count
(LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a
Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe)
bus, a serial advanced technology attachment (SATA) bus, a Video
Electronics Standards Association local (VLB) bus, or another
suitable bus or a combination of two or more of these. Bus 612 may
include one or more buses 612, where appropriate. Although this
disclosure describes and illustrates a particular bus, this
disclosure contemplates any suitable bus or interconnect.
[0054] Herein, a computer-readable non-transitory storage medium or
media may include one or more semiconductor-based or other
integrated circuits (ICs) (such, as for example, field-programmable
gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk
drives (HDDs), hybrid hard drives (HHDs), optical discs, optical
disc drives (ODDs), magneto-optical discs, magneto-optical drives,
floppy diskettes, floppy disk drives (FDDs), magnetic tapes,
solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or
drives, any other suitable computer-readable non-transitory storage
media, or any suitable combination of two or more of these, where
appropriate. A computer-readable non-transitory storage medium may
be volatile, non-volatile, or a combination of volatile and
non-volatile, where appropriate.
[0055] Herein, "or" is inclusive and not exclusive, unless
expressly indicated otherwise or indicated otherwise by context.
Therefore, herein, "A or B" means "A, B, or both," unless expressly
indicated otherwise or indicated otherwise by context. Moreover,
"and" is both joint and several, unless expressly indicated
otherwise or indicated otherwise by context. Therefore, herein, "A
and B" means "A and B, jointly or severally," unless expressly
indicated otherwise or indicated otherwise by context.
[0056] The scope of this disclosure encompasses all changes,
substitutions, variations, alterations, and modifications to the
example embodiments described or illustrated herein that a person
having ordinary skill in the art would comprehend. The scope of
this disclosure is not limited to the example embodiments described
or illustrated herein. Moreover, although this disclosure describes
and illustrates respective embodiments herein as including
particular components, elements, feature, functions, operations, or
steps, any of these embodiments may include any combination or
permutation of any of the components, elements, features,
functions, operations, or steps described or illustrated anywhere
herein that a person having ordinary skill in the art would
comprehend. Furthermore, reference in the appended claims to an
apparatus or system or a component of an apparatus or system being
adapted to, arranged to, capable of, configured to, enabled to,
operable to, or operative to perform a particular function
encompasses that apparatus, system, component, whether or not it or
that particular function is activated, turned on, or unlocked, as
long as that apparatus, system, or component is so adapted,
arranged, capable, configured, enabled, operable, or operative.
Additionally, although this disclosure describes or illustrates
particular embodiments as providing particular advantages,
particular embodiments may provide none, some, or all of these
advantages.
* * * * *