The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for setting ad breakpoints in a video. The program and method provide for accessing a video; determining plural shot boundaries for the video, each shot boundary defining a shot corresponding to a contiguous sequence of video frames that is free of cuts or transitions; and for each shot boundary of the plural shot boundaries, performing a set of breakpoint tests on the shot boundary, each breakpoint test configured to return a respective score indicating whether the shot boundary corresponds to a breakpoint for potential insertion of an ad during playback of the video, calculating a combined score for the shot boundary based on combining the each of the respective scores, and setting, in a case where the combined score meets a threshold value, the shot boundary as the breakpoint.

A method for generating a slow motion video. The method includes segmenting, by an electronic device, objects in the video. Further, the method includes determining, by the electronic device, an interaction between the segmented objects. Further, the method includes clustering, by the electronic device, the segmented objects in the video to generate object clusters based on the interaction. Further, the method includes determining, by the electronic device, a degree of slow motion effect to be applied to each of the object clusters in the video based on a significance score of each of the object clusters. Further, the method includes generating, by the electronic device, the slow motion video by applying the degree of slow motion effect to that has been determined to corresponding the object clusters.

A method, a system, and a computer program product for adapting video content to mitigate adverse health effects in users. A data file uploaded to a first storage location is detected. The data file is tagged upon determining a presence of one or more triggering content. At least one of a location and a type of the triggering content in the data file are determined. One or more timestamps identifying the location of the triggering content are inserted in the data file. A modified data file is generated and a playback of the modified data file is executed.

A method performed by a portable media player device. The method receives a microphone signal that includes audio content output by an audio playback device via a loudspeaker. The method determines identification information regarding the audio content, wherein the identification information is determined through an acoustic signal analysis of the microphone signal. In response to determining that the audio playback device has ceased outputting the audio content, the method retrieves from local memory of the portable media player device or a remote device with which the portable media player device is communicatively coupled, an audio signal that corresponds to the audio content and drives a speaker that is not part of the audio playback device using the audio signal to continue outputting the audio content and any additional audio content related to the audio content.

Disclosed herein are a visual story generation method and apparatus for generating a story from a plurality of images by using a deep learning network. The visual story generation method includes: extracting features from a plurality of respective images by using the first extraction unit of a deep learning network; generating the structure of a story based on the overall feature of the plurality of images by using the second extraction unit of the deep learning network; and generating the story by using outputs of the first and second extraction units.

A system and method that guides a user to record initial and final walkthrough videos, along with audio, of their rental property is disclosed. The embodiments also provide a system that can scan a lease for the rental property, as well as audio from a user's narration of the video, and extract and tag particular features of the video for efficient querying at a later time. The system can interpret portions of the lease that are relevant to particular items or components in the apartment (for example, major appliances) and guide the user to perform a comprehensive inspection of the items mentioned in the lease.

Methods, systems, and computer media provide for identifying exclusion zones in frames of a video, aggregating those exclusion zones for a specified duration or number of frames, defining a inclusion zone within which overlaid content is eligible for inclusion, and providing overlaid content for inclusion in the inclusion zone. The exclusion zones can include regions in which significant features are detected such as text, human features, objects from a selected set of object categories, or moving objects.

A method for generating a synopsis video includes acquiring a target video and parameter data related to editing of the target video, wherein the parameter data comprises at least a duration parameter of a synopsis video of the target video; extracting a plurality of pieces of image data from the target video, and determining an image label of the image data, wherein the image label comprises at least a visual-type label; and determining a type of the target video, and establishing a target editing model for the target video according to the type of the target video, the duration parameter, and a plurality of preset editing technique submodels; and editing the target video according to the image label of the image data in the target video by using the target editing model to obtain the synopsis video of the target video

A method and an electronic device are provided for editing video using objects of interest included in the video. An electronic device includes a camera, a memory, and a processor. The processor may obtain first video data through the camera, while obtaining the first video data, determine a first object of interest in a plurality of image frames included in the first video data, store, in the memory, second video data generated from the first video data based on the first object of interest, after generating the second video data, receive a user input for determining a second object of interest, different from the first object of interest, in the plurality of the image frames included in the first video data, and in response to receiving the user input, store, in the memory, third video data generated from the first video data based on at least the second object of interest.