Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: A system identifies a video comprising frames associated with content tags. The system detects features for each frame of the video. The system identifies, based on the detected features, scenes of the video. The system determines, for each frame for each scene, a frame score that indicates a number of content tags that match the other frames within the scene. The system selects, for each scene, a set of key frames that represent the scene based on the determined frame scores. The system receives a search query comprising a keyword. The system generates, for display, search results responsive to the search query including a dynamic preview of the video. The dynamic preview comprises an arrangement of frames of the video corresponding to each scene of the video. Each of the arrangement of frames is selected from the selected set of key frames representing the respective scene of the video.

Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: A translation system provides machine translations of review texts on item pages using context from the item pages outside of the review text being translated. Given review text from an item page, context for machine translating the review text is determined from the item page. In some aspects, one or more keywords are determined based on text, images, and/or videos on the item page. The one or more keywords are used as context by the machine translator to translate the review text from a first language to a second language to provide translated review text, which can be presented on the item page.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting changes to a point of interest between a selected version and a previous version of a digital image and providing a summary of the changes to the point of interest. For example, the disclosed system provides for display a selected version of a digital image and detects a point of interest within the selected version of the digital image. The disclosed system determines image modifications to the point of interest (e.g., tracks changes to the point of interest) to generate a summary of the image modifications. Moreover, the summary can indicate further information concerning image modifications applied to the selected point of interest, such as timestamp, editor, or author information.

Abstract: Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that can generate contextual identifiers indicating context for frames of a video and utilize those contextual identifiers to generate translations of text corresponding to such video frames. By analyzing a digital video file, the disclosed systems can identify video frames corresponding to a scene and a term sequence corresponding to a subset of the video frames. Based on images features of the video frames corresponding to the scene, the disclosed systems can utilize a contextual neural network to generate a contextual identifier (e.g. a contextual tag) indicating context for the video frames. Based on the contextual identifier, the disclosed systems can subsequently apply a translation neural network to generate a translation of the term sequence from a source language to a target language. In some cases, the translation neural network also generates affinity scores for the translation.

Abstract: Techniques are described for trace layer for replicating a source region of a digital image. In an implementation, a user leverages a content editing system to select a source region of a source image to be replicated and a target region of a target image to which portions of the source region are to be replicated. A trace layer is generated that is a visual representation of portions of the source region, and the trace layer is positioned on the target region of the target image. Further, the trace layer is generated based on a visibility factor such that the trace layer is at least partially transparent. The trace layer receives user interaction to select portions of the trace layer and visibility of the selected portions is modified to replicate corresponding portions of the source region to the target region.

Abstract: A system identifies a video comprising frames associated with content tags. The system detects features for each frame of the video. The system identifies, based on the detected features, scenes of the video. The system determines, for each frame for each scene, a frame score that indicates a number of content tags that match the other frames within the scene. The system selects, for each scene, a set of key frames that represent the scene based on the determined frame scores. The system receives a search query comprising a keyword. The system generates, for display, search results responsive to the search query including a dynamic preview of the video. The dynamic preview comprises an arrangement of frames of the video corresponding to each scene of the video. Each of the arrangement of frames is selected from the selected set of key frames representing the respective scene of the video.

Abstract: Certain embodiments involve adaptive search results for multimedia search queries to provide dynamic previews. For instance, a computing system receives a search query that includes a keyword. The computing system identifies, based on the search query, a video file having keyframes with content tags that match the search query. The computing system determines matching scores for respective keyframes of the identified video file. The computing system generates a dynamic preview from at least two keyframes having the highest matching scores.

Abstract: Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that can generate contextual identifiers indicating context for frames of a video and utilize those contextual identifiers to generate translations of text corresponding to such video frames. By analyzing a digital video file, the disclosed systems can identify video frames corresponding to a scene and a term sequence corresponding to a subset of the video frames. Based on images features of the video frames corresponding to the scene, the disclosed systems can utilize a contextual neural network to generate a contextual identifier (e.g. a contextual tag) indicating context for the video frames. Based on the contextual identifier, the disclosed systems can subsequently apply a translation neural network to generate a translation of the term sequence from a source language to a target language. In some cases, the translation neural network also generates affinity scores for the translation.

Abstract: Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.

Abstract: Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize a multi-panel graphical user interface for modifying digital images. For example, in one or more embodiments, the disclosed systems divide the graphical user interface of a client device into first panel and a second panel. Further, the disclosed systems provide different portions of a digital image for display within the first and second panels. In some implementations, the disclosed systems receive a user interaction with the portion of the digital image displayed within the first panel. Based on the received user interaction, the disclosed systems modify the second portion of the digital image displayed within the second panel.

Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that can generate contextual identifiers indicating context for frames of a video and utilize those contextual identifiers to generate translations of text corresponding to such video frames. By analyzing a digital video file, the disclosed systems can identify video frames corresponding to a scene and a term sequence corresponding to a subset of the video frames. Based on images features of the video frames corresponding to the scene, the disclosed systems can utilize a contextual neural network to generate a contextual identifier (e.g. a contextual tag) indicating context for the video frames. Based on the contextual identifier, the disclosed systems can subsequently apply a translation neural network to generate a translation of the term sequence from a source language to a target language. In some cases, the translation neural network also generates affinity scores for the translation.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can generate a context-aware-video-progress bar including a video-scene-proportionate timeline with time-interval sections sized according to relative scene proportions within time intervals of a video. In some implementations, for instance, the disclosed systems determine relative proportions of scenes within a video across time intervals of the video and generate a video-scene-proportionate timeline comprising time-interval sections sized proportionate to the relative proportions of scenes across the time intervals. By integrating the video-scene-proportionate timeline within a video-progress bar, the disclosed systems generate a context-aware-video-progress bar for a video.

Abstract: Certain embodiments involve adaptive search results for multimedia search queries to provide dynamic previews. For instance, a computing system receives a search query that includes a keyword. The computing system identifies, based on the search query, a video file having keyframes with content tags that match the search query. The computing system determines matching scores for respective keyframes of the identified video file. The computing system generates a dynamic preview from at least two keyframes having the highest matching scores.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can generate a context-aware-video-progress bar including a video-scene-proportionate timeline with time-interval sections sized according to relative scene proportions within time intervals of a video. In some implementations, for instance, the disclosed systems determine relative proportions of scenes within a video across time intervals of the video and generate a video-scene-proportionate timeline comprising time-interval sections sized proportionate to the relative proportions of scenes across the time intervals. By integrating the video-scene-proportionate timeline within a video-progress bar, the disclosed systems generate a context-aware-video-progress bar for a video.