WO2024160260A1

WO2024160260A1 - Video processing method and apparatus, device, and storage medium

Info

Publication number: WO2024160260A1
Application number: PCT/CN2024/075333
Authority: WO
Inventors: 李亚; 周文
Original assignee: 北京有竹居网络技术有限公司
Priority date: 2023-02-01
Filing date: 2024-02-01
Publication date: 2024-08-08
Also published as: CN116112743A

Abstract

The embodiments of the present disclosure relate to a video processing method and apparatus, a device, and a storage medium. The method provided herein comprises: based on access information of a first video, determining a video clip from the first video, the access information indicating the distribution over the time of the video of access statistics data of the first video; determining the semantic continuity of the video clip and the first video; and in response to the semantic continuity exceeding a threshold, adding the video clip in front of the first video, thereby generating a second video. Based on the above method, the embodiments of the present disclosure can make a video more attractive to a user by adding in front of the video a specific clip from the video.

Description

Video processing method, device, equipment and storage medium

This application claims priority to the Chinese invention patent application entitled “Video Processing Method, Device, Equipment and Storage Medium” filed on February 1, 2023 and application number 202310126804.5, the entire contents of which are incorporated by reference into this application.

Technical Field

Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to methods, devices, apparatuses, and computer-readable storage media for video processing.

Background Art

With the development of computer technology, various video contents have become one of the main ways for people to obtain content. Especially for some short video contents, people usually determine whether they are interested in them through some of the contents played initially, and decide whether to continue watching the subsequent contents or switch to play other video contents.

Summary of the invention

In a first aspect of the present disclosure, a method for video processing is provided. The method comprises: determining a video segment from the first video based on access information of the first video, wherein the access information indicates the distribution of access statistics of the first video over video time; determining semantic continuity between the video segment and the first video; and in response to the semantic continuity being higher than a threshold, generating a second video by appending the video segment to the front of the first video.

In a second aspect of the present disclosure, a device for video processing is provided. The device includes: a determination module configured to determine a video segment from a first video based on access information of the first video, the access information indicating the distribution of access statistics of the first video over video time; a judgment module configured to determine semantic continuity between the video segment and the first video; and an editing module configured to, in response to the semantic continuity being higher than a threshold, edit the video segment by editing the video segment. The segment is appended to the first video to generate the second video.

In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the device executes the method of the first aspect.

In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to implement the method of the first aspect.

It should be understood that the contents described in this content section are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

FIG1 shows a schematic diagram of an example environment in which embodiments according to the present disclosure may be implemented;

FIG2 shows a flow chart of an example process of video processing according to some embodiments of the present disclosure;

FIG3 shows a schematic structural block diagram of a device for video processing according to some embodiments of the present disclosure; and

FIG. 4 illustrates a block diagram of an electronic device capable of implementing various embodiments of the present disclosure.

DETAILED DESCRIPTION

The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. The disclosed drawings and embodiments are only for illustrative purposes and are not intended to limit the protection scope of the present disclosure.

It should be noted that the titles of any sections/subsections provided herein are not restrictive. Various embodiments are described throughout this article, and any type of embodiment may be included under any section/subsection. In addition, the embodiments described in any section/subsection may be combined in any manner with any other embodiments described in the same section/subsection and/or different sections/subsections.

In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below. The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

The embodiments of the present disclosure may involve user data, data acquisition and/or use, etc. These aspects are subject to the corresponding laws, regulations and relevant provisions. In the embodiments of the present disclosure, all data collection, acquisition, processing, processing, forwarding, use, etc. are carried out on the premise that the user knows and confirms. Accordingly, when implementing each embodiment of the present disclosure, the type, scope of use, usage scenario, etc. of the data or information that may be involved should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with the relevant laws and regulations. The specific notification and/or authorization method can vary according to the actual situation and application scenario, and the scope of the present disclosure is not limited in this respect.

In this specification and the embodiments, if personal information processing is involved, it will be processed on the premise of having a legal basis (such as obtaining the consent of the subject of personal information, or it is necessary to perform a contract, etc.), and will only be processed within the scope of regulations or agreements. If a user refuses to process personal information other than the necessary information for basic functions, it will not affect the user's use of basic functions.

As briefly mentioned above, for video content, the initial content of the video has a great influence on whether the user continues to watch the video. The attractive content is not always located at the top of the video, which may cause people to miss such parts. In addition, manually selecting the highlights in the video requires a lot of manpower and may have the defect of insufficient accuracy.

The embodiment of the present disclosure proposes a scheme for video processing. According to the scheme, a video segment can be determined from a first video based on access information of the first video, wherein the access information indicates the distribution of access statistics of the first video over the video time. Further, the semantic continuity between the video segment and the first video can be determined. If the semantic continuity is higher than a threshold, a second video can be generated by appending the video segment to the front of the first video.

In this way, the embodiment of the present disclosure can automatically determine the video clips that the user may be interested in from the video, and if the semantic continuity between the video clips and the original video is good, the video clips are added to the original video. Therefore, the embodiment of the present disclosure can improve the attractiveness of the video content and ensure the semantic continuity of the video content.

Various example implementations of the solution are described in detail below in conjunction with the accompanying drawings.

Example Environment

FIG1 shows a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. As shown in FIG1 , the environment 100 may include a processing device 120. The processing device 120 may include any suitable electronic device, examples of which may include but are not limited to: a mobile device, a tablet computer, a laptop computer, a desktop computer, a cloud server, an edge computing device, etc.

As shown in FIG1 , the processing device 120 may obtain the first video 110 and the access information 130 of the first video 110. In some embodiments, the first video 110 may be a video released by a creator. Exemplarily, the first video 110 may include an advertisement video, which may receive a click operation from a user and perform corresponding interactions, such as guiding to a corresponding promotional page or purchase page.

In some embodiments, the access information 130 may indicate the distribution of access statistics of the first video 110 over the video time. Such access statistics may include, for example, the user click rate and/or user churn rate of the first video 110. It should be understood that if the user click rate of a video at a certain moment is high, it may indicate that the user is more interested in the content at that moment; on the contrary, if the user churn rate of a video at a certain moment is high (i.e., the user stops watching the video), the user may be more interested in the content at that moment. The proportion of users at that moment may indicate that the users are less interested in the content at that moment.

1 , the processing device 120 may determine a video segment 140 from the first video 110 according to the access information 130 of the first video 110. Such a video segment 140 may be part of the content of the first video 110, or may be generated based on part of the content of the first video 110.

Further, the processing device 120 may determine the semantic continuity between the video segment 140 and the first video 110 , and if the semantic continuity is above a threshold, append the video segment 140 to the first video 110 to generate the second video 150 .

For example, the video segment 140 may be edited as the header of the second video 150. The process of generating the video segment 140 and determining the semantic continuity will be described in detail below in conjunction with FIG.

It should be understood that the structure and functionality of environment 100 are described for exemplary purposes only and does not imply any limitation on the scope of the present disclosure.

Example Process

2 shows a flow chart of an example process 200 for video processing according to some embodiments of the present disclosure. Process 200 may be implemented at processing device 120. Process 200 is described below with reference to FIG.

As shown in FIG. 2 , at block 210 , the processing device 120 determines a video segment 140 from the first video 110 based on access information 130 of the first video 110 , wherein the access information 130 indicates distribution of access statistics of the first video 110 over video time.

In some embodiments, the processing device 120 may obtain the first video 110, such that the first video 110 may be, for example, a video that has been published. Exemplarily, the processing device 120 may determine the first video 110 from the video collection based on the number of views and/or clicks of the video in the video collection. For example, the processing device 120 may obtain the first video 110 whose number of views is greater than a preset number from a video library accessible to the public based on the number of views and/or clicks of the video.

Furthermore, the processing device 120 may also obtain access information 130 of the first video 110. Such access information 130 is used to indicate the distribution of the user's interest in the first video 110 over the video time.

Taking the first video 110 as an advertisement video as an example, the access information 130 may include, for example, the distribution of the video click rate and/or video loss rate of the first video 110 along with the video time of the first video 110 .

In order to determine the video segment 140 that the user may be more interested in from the first video 110, the processing device 120 may determine the time interval corresponding to the segment based on the access statistics. Specifically, the processing device 120 may determine the target moment of the first video 110 based on the access information 130, wherein the access statistics of the first video at the target moment meet the threshold requirement. Exemplarily, such a target moment may be the moment when the user click rate of the first video 110 is the highest and/or the moment when the user churn rate is the lowest.

Furthermore, in order to ensure that the determined video segment is semantically continuous, the processing device 120 may also determine a time interval associated with the target moment based on semantic recognition of the text content of the first video 110, wherein the text segment corresponding to the time interval has continuous semantics.

For example, the processing device 120 may recognize the speech of the first video 110 to obtain its text content. Additionally, the processing device 120 may obtain a text segment with continuous semantics associated with the target moment based on semantic recognition of the text content.

In some embodiments, the time length of such a text segment (ie, the length of the determined time interval) needs to meet a preset length range. For example, the processing device 120 may determine a time interval of 3 seconds to 7 seconds based on the target time and semantic information.

In some embodiments, in addition to considering semantic continuity, the processing device 120 may also add punctuation to the text content, and determine a single complete sentence associated with the target moment based on the text content after the punctuation is added.

Based on this approach, the embodiments of the present disclosure can ensure that the text content corresponding to the determined time interval is semantically continuous and complete.

Furthermore, the processing device 120 may obtain a video segment 140 corresponding to the time interval. Exemplarily, the processing device 120 may directly determine the segment of the first video 110 corresponding to the time interval as the video segment 140.

In some embodiments, in order to ensure that the generated video segment 140 is visually readable, the processing device 120 may also use an appropriate storyboard model (e.g., TransNet V2 Model) is used to divide the first video 110 into a group of storyboard segments, each of which may correspond to a different storyboard, for example.

Additionally, if the determined time interval is associated with a plurality of storyboard segments, the processing device 120 may generate a video segment based on the plurality of storyboard segments. For example, the processing device 120 may generate the video segment 140 by combining the plurality of storyboard segments. Alternatively, the processing device 120 may also add a smoothing effect such as fade-in and fade-out between the plurality of storyboard segments to construct the video segment 140.

In this way, the embodiments of the present disclosure can provide a video segment 140 that is semantically continuous, semantically complete, and storyboard-continuous.

In some embodiments, if the determined target moment falls within the target time range, the processing device 120 may not generate the video segment 140. Such a target time range may, for example, include a first preset duration associated with the start moment of the first video 110 (e.g., the first five seconds of the video), and/or a second preset duration associated with the end moment of the first video 110 (e.g., the last five seconds of the video).

On the contrary, if the determined target moment does not fall within the target time range, the processing device 120 may perform determination of a time interval to generate the video segment 140 .

Continuing with reference to FIG. 2 , at block 220 , the processing device 120 determines semantic continuity of the video segment 140 with the first video 110 .

In some embodiments, in order to ensure that the added header has good continuity with the original video, the processing device 120 may determine semantic continuity based on the features of the video segment 140 and the features of the first video 110 .

In some embodiments, the processing device 120 may process features of the video segment 140 and the first video 110 using an analysis model to determine semantic continuity, wherein the features include at least one of the following: visual features of the video, speech features of the video, or text features of the video.

Exemplarily, the processing device 120 may utilize an appropriate machine learning model as an analysis model, and may use visual features, speech features, text features and/or other appropriate features or feature combinations of the two videos as inputs to the analysis model to determine the semantic continuity between the two videos.

In some embodiments, in order to train a machine learning model to have the ability to judge the semantics of a video To improve the ability of continuous training, a suitable training device (which may be the same as or different from the processing device 120) can use sample data to analyze the model. Such sample data may include positive sample data and/or negative sample data, for example.

In some embodiments, such positive sample data may include, for example, a first video segment and a second video segment, wherein the first video segment is related to a first semantic continuous shot of a reference video, and the second video segment is another video segment in the reference video that is different from the first video segment.

For example, the training device can extract a semantically continuous shot from a published reference video based on the storyboard model. Further, the training device can determine the video segment corresponding to such a shot and other video segments of the reference video as positive sample data to indicate that such two video segments are semantically continuous.

In some embodiments, such negative sample data may include a third video segment and a fourth video segment from different videos to indicate that such video segments are discontinuous.

Based on this approach, the embodiments of the present disclosure can automatically and efficiently determine the semantic continuity between the generated video clip and the original video content.

At block 230 , in response to the semantic continuity being above the threshold, the processing device 120 generates the second video 150 by appending the video segment 140 to the front of the first video 110 .

Exemplarily, the analysis model may output the semantic continuity as a continuous numerical value (eg, a value between 0 and 1 to indicate its semantic continuity), or may output the semantic continuity as a discrete numerical value (eg, 0 for discontinuity and 1 for continuity).

If such semantic continuity is above a threshold (eg, the continuous value is greater than a certain threshold value, and the discrete value is greater than 0), the processing device 120 may determine that the generated video segment 140 is suitable for being appended to the first video 110 .

Furthermore, the processing device 120 generates the second video 150 by editing the video clip 140 before the first video 110. Thus, the generated second video 150 can have a title content that is more attractive to users, and does not affect the continuous viewing experience of the second video 150.

In some embodiments, the processing device 120 may further publish the edited second video 150 .

Thus, the embodiments of the present disclosure can utilize the delayed knowledge of the video (e.g., Access information) to perform intelligent editing of videos, thereby creating video content that is more attractive to users and ensuring the viewing experience of such video content.

Example devices and equipment

The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes.

3 shows a schematic structural block diagram of an apparatus 300 for video processing according to some embodiments of the present disclosure. The apparatus 300 may be implemented as or included in the processing device 120. Each module/component in the apparatus 300 may be implemented by hardware, software, firmware or any combination thereof.

The apparatus 300 comprises a determination module 310 configured to determine a video segment from the first video based on access information of the first video, wherein the access information indicates a distribution of access statistics of the first video over video time.

The apparatus 300 further includes a determination module 320 configured to determine semantic continuity between the video segment and the first video.

In addition, the apparatus 300 further comprises an editing module 330 configured to generate a second video by appending a video segment to the front of the first video in response to the semantic continuity being higher than a threshold.

In some embodiments, the determination module 310 is further configured to: determine the target moment of the first video based on the access information, wherein the access statistics of the first video at the target moment meet the threshold requirement; determine the time interval associated with the target moment based on the semantic recognition of the text content of the first video, wherein the text segment corresponding to the time interval has continuous semantics; and obtain the video segment corresponding to the time interval.

In some embodiments, the determination module 310 is further configured to: divide the first video into a group of storyboard segments using a storyboard model; and generate a video segment based on the multiple storyboard segments in response to the time interval being associated with the multiple storyboard segments.

In some embodiments, the length of the time interval is within a preset length range.

In some embodiments, the text segment corresponds to a single complete sentence in the text content, and the single complete sentence is determined based on adding punctuation to the text content.

In some embodiments, the determination module 310 is further configured to: in response to the target time not falling within the target time range, determine a time interval, wherein the target time range includes: A first preset duration associated with a start time of a video, and/or a second preset duration associated with an end time of the first video.

In some embodiments, the judgment module 320 is further configured to: use the analysis model to process features of the video clip and the first video to determine semantic continuity, the features including at least one of the following: visual features of the video, voice features of the video, or text features of the video.

In some embodiments, the analysis model is trained based on the following sample data: positive sample data, including a first video clip and a second video clip, the first video clip is related to the first semantic continuous shot of a reference video, and the second video is other video clips in the reference video that are different from the first video clip; or negative sample data, including a third video clip and a fourth video clip from different videos.

In some embodiments, the access statistics indicate at least: video click-through rate and/or user churn rate.

In some embodiments, the apparatus 300 further includes a video selection module configured to determine a first video from the video set based on the number of plays and/or clicks of the videos in the video set.

In some embodiments, the first video comprises an advertisement video.

FIG4 shows a block diagram of an electronic device 400 in which one or more embodiments of the present disclosure may be implemented. It should be understood that the electronic device 400 shown in FIG4 is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. The electronic device 400 shown in FIG4 may be used to implement the processing device 120 of FIG1 .

As shown in FIG4 , the electronic device 400 is in the form of a general electronic device. The components of the electronic device 400 may include, but are not limited to, one or more processors or processing units 410, a memory 420, a storage device 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. The processing unit 410 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 420. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 400.

The electronic device 400 typically includes a plurality of computer storage media. Such media may be any accessible media that the electronic device 400 can access, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 420 may be a volatile memory. The storage device 430 may be a removable or non-removable medium and may include a machine-readable medium such as a flash drive, a disk, or any other medium that may be capable of storing information and/or data (e.g., training data for training) and may be accessed within the electronic device 400.

The electronic device 400 may further include additional removable/non-removable, volatile/non-volatile storage media. Although not shown in FIG. 4 , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a “floppy disk”) and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 420 may include a computer program product 425 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

The communication unit 440 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 400 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 400 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

The input device 450 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 460 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 400 may also communicate with one or more external devices (not shown) through the communication unit 440 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 400, or communicate with any device that allows the electronic device 400 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input/output (I/O) interface (not shown).

According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

Various aspects of the present disclosure are described herein with reference to the flowcharts and/or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and/or block diagram and the combination of each box in the flowchart and/or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

The flowcharts and block diagrams in the accompanying drawings show possible architectures, functions, and operations of systems, methods, and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes may actually be executed substantially in parallel, and they may sometimes be executed in the opposite order. It depends on the functions involved. It should also be noted that each block in the block diagram and/or flow chart, and combinations of blocks in the block diagram and/or flow chart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

A video processing method, comprising:

Determining a video segment from the first video based on access information of the first video, the access information indicating a distribution of access statistics of the first video over video time;

determining semantic continuity between the video segment and the first video; and

In response to the semantic continuity being higher than a threshold, a second video is generated by appending the video segment to the front of the first video.
The method of claim 1, wherein determining the video segment comprises:

Determining a target time of the first video based on the access information, wherein the access statistics data of the first video at the target time meets a threshold requirement;

Determining a time interval associated with the target moment based on semantic recognition of text content of the first video, wherein a text segment corresponding to the time interval has continuous semantics; and

The video segment corresponding to the time interval is obtained.
The method according to claim 2, wherein obtaining the video segment corresponding to the time interval comprises:

Using the storyboard model, dividing the first video into a group of storyboard segments; and

In response to the time interval being associated with a plurality of storyboard segments, the video segment is generated based on the plurality of storyboard segments.
The method according to claim 2, wherein the length of the time interval is within a preset length range.
The method according to claim 2, wherein the text segment corresponds to a single complete sentence in the text content, and the single complete sentence is determined based on adding punctuation to the text content.
The method according to claim 2, wherein determining the time interval comprises:

In response to the target moment not falling within the target time range, the time interval is determined, wherein the target time range includes: a first preset duration related to the start moment of the first video, and/or a second preset duration related to the end moment of the first video.
The method of claim 1, wherein determining semantic continuity between the video segment and the first video comprises:

The semantic continuity is determined by processing features of the video clip and the first video using an analysis model, wherein the features include at least one of the following: visual features of the video, voice features of the video, or text features of the video.
The method according to claim 7, wherein the analysis model is trained based on the following sample data:

Positive sample data includes a first video segment and a second video segment, wherein the first video segment is related to the first semantic continuous shot of a reference video, and the second video segment is another video segment in the reference video that is different from the first video segment; or

The negative sample data includes a third video segment and a fourth video segment from different videos.
The method according to claim 1, wherein the access statistics at least indicate: video click-through rate and/or user churn rate.
The method according to claim 1, further comprising:

A first video is determined from the video set based on the number of views and/or clicks of the videos in the video set.
The method of claim 1, wherein the first video comprises an advertisement video.
A device for video processing, comprising:

a determination module configured to determine a video segment from a first video based on access information of the first video, wherein the access information indicates a distribution of access statistics of the first video over video time;

a determination module, configured to determine semantic continuity between the video segment and the first video; and

The editing module is configured to generate a second video by appending the video segment to the front of the first video in response to the semantic continuity being higher than a threshold.
An electronic device, comprising:

at least one processing unit; and

At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 11 when executed by the at least one processing unit.
A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 11.