Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated identification and mapping of objects in video content. Example methods may include determining a first set of frames in video content, determining, using one or more object recognition algorithms, a first object present in the first set of frames, determining that a first product corresponding to the first object is present in a product catalog comprising a set of product images, associating a first product identifier of the first product with a video identifier of the video content, and causing presentation of a set of product identifiers associated with the video identifier.

Abstract: Techniques for using a machine learning model to determine a proper subset of a multimedia file for a viewer based on their interest without the need to actively control a media player timeline are described.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated identification and mapping of objects in video content. Example methods may include determining a first set of frames in video content, determining, using one or more object recognition algorithms, a first object present in the first set of frames, determining that a first product corresponding to the first object is present in a product catalog comprising a set of product images, associating a first product identifier of the first product with a video identifier of the video content, and causing presentation of a set of product identifiers associated with the video identifier.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated identification and mapping of objects in video content. Example methods may include determining a first set of frames in video content, determining, using one or more object recognition algorithms, a first object present in the first set of frames, determining that a first product corresponding to the first object is present in a product catalog comprising a set of product images, associating a first product identifier of the first product with a video identifier of the video content, and causing presentation of a set of product identifiers associated with the video identifier.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for semantic video segmentation to identify objects appearing in video content. Example methods may include determining a voice-based request at a first device to identify an object appearing in video content, determining a first video identifier for video content being consumed in association with the first device, determining a timestamp associated with the video content at a time the voice-based request was received, determining, using one or more knowledge graphs, a set of product identifiers for products present in the video content at a scene corresponding to the timestamp, and generating a response to the voice-based request comprising a first product identifier in the set of product identifiers.

Abstract: Techniques are described for detecting desynchronization between an audio component and a video component of a media presentation. Feature sets may be determined for portions of the audio component and portions of the video component, which may then be used to generate correlations between portions of the audio component and portions of the video component. Synchronization may then be assessed based on the correlations.

Abstract: This disclosure is directed to a system and method that detects duplicated content and/or media items. A media item can be split into media item portions. Based on the media item portions, features can be determined. Using the features, media item portion signatures can be determined to generate a media item signature. The media item signature can be compared with a different media item signature to determine duplicated content within the media items.

Abstract: Systems and methods are provided for detecting and correcting synchronization errors in multimedia content comprising a video stream and a non-original audio stream. Techniques for directly detecting synchronization of video and audio streams may be inadequate to detect synchronize errors for non-original audio streams, particularly where such non-original audio streams contain audio not reflective of events within the video stream, such as speaking dialog in a different language than the speakers of the video stream. To overcome this problem, the present disclosure enables synchronization of a non-original audio stream to another audio stream, such as an original audio stream, that is synchronized to the video stream. By comparison of signatures, the non-original and other audio stream are aligned to determine an offset that can be used to synchronize the non-original audio stream to the video stream.

Abstract: Intelligent reframing techniques are described in which content (e.g., a movie) can be generated in a different aspect ratio than previously provided. These techniques include obtaining various video frames having a first aspect ratio. Various objects can be identified within the frames. An object having the highest degree of importance in a frame can be selected and a focal point can be calculated based at least in part on that object. A modified version of the content can be generated in a second aspect ratio that is different from the first aspect ratio. The modified version can be generated using the focal point calculated based on the object having the greatest degree of importance. Using these techniques, the content can be provided in a different aspect ratio while ensuring that the most important features of the frame still appear in the new version of the content.

Abstract: A system can be configured to receive entertainment content requested by a user and identify content segments and content features from the entertainment content. The content segments can be utilized to identify portions of the entertainment content for enrichment and/or enhancement by the system. The content features can be utilized to associate the entertainment content and the content segments with supplemental content that includes or is associated with the content features. The content features can indicate genres, scene classifications, significant figures credited with creating the entertainment content, and other points of interests for users interested in the entertainment content. The associations between the entertainment content and the supplemental content can enable the system to engage the users by presenting the supplemental content determined to match interests of the users.

Abstract: This disclosure is directed to a system and method that automatically detects repeated content within multiple media items. Content providers often include content, such as an introduction, near the beginning of a media item. In some circumstances, such as in the case of a series of television episodes, the content providers use the same content in each episode of the series. By dividing the media items into portions and analyzing the portions, the systems and methods described can automatically detect the repeated content. Using the detection of the repeated content, a user interface can then allow a user to bypass the repeated content during playback.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated identification and mapping of objects in video content. Example methods may include determining a first set of frames in video content, determining, using one or more object recognition algorithms, a first object present in the first set of frames, determining that a first product corresponding to the first object is present in a product catalog comprising a set of product images, associating a first product identifier of the first product with a video identifier of the video content, and causing presentation of a set of product identifiers associated with the video identifier.

Abstract: Technologies are provided for the detection and recognition of overlaid content within video content. Some embodiments include a computing system that can receive data defining a sequence of frames corresponding to video content. The sequence of frames spans a defined time interval. The computing system can determine image changes between contiguous images defined by contiguous frames in the sequence of frames. A subset of the image changes can indicate static content within the video content, and another subset of the image changes can indicate non-static content. The computing system can then generate a composite image using at least the image changes, where the composite image includes an area representing the static content. Using the composite image, the computing system can classify the area as a defined visual element. Examples of the defined visual element include a logo and text.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated selection of color palettes for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment comprising a first set of frames, determining, using a first video processing algorithm, a first object that is present in the first set of frames, and determining, using a second video processing algorithm, a first semantic characteristic of the first segment. Some example methods may include generating a first vector representing the first object and the first semantic characteristic, and generating, using a first neural network and the first vector, a first color palette recommendation for the first segment. Selection of the first color palette recommendation may cause a color filter to be applied to the first set of frames.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model.

Abstract: The embodiments provide systems and methods for efficiently and accurately differentiating requests directed to uncacheable content from requests directed to cacheable content based on identifiers from the requests. The differentiation occurs without analysis or retrieval of the content being requested. Some embodiments hash identifiers of prior requests that resulted in uncacheable content being served in order to set indices within a bloom filter. The bloom filter then tracks prior uncacheable requests without storing each of the identifiers so that subsequent requests for uncacheable requests can be easily identified based on a hash of the request identifier and set indices of the bloom filter. Some embodiments produce a predictive model identifying uncacheable content requests by tracking various characteristics found in identifiers of prior requests that resulted in uncacheable content being served.

Abstract: Techniques are described for detecting and replacing burned-in subtitles in image and video content.

Abstract: A user may indicate an interest relating to events such as objects, persons, or activities, where the events included in content depicted in a video. The user may also indicate a configurable action associated with the user interest, including receiving a notification via an electronic device. A video item, for example a live-streaming sporting event, may be broken into frames and analyzed frame-by-frame to determine a region of interest. The region of interest is then analyzed to identify objects, persons, or activities depicted in the frame. In particular, the region of interest is compared to stored images that are known to depict different objects, persons, or activities. When a region of interest is determined to be associated with the user interest, the configurable action is triggered.

Abstract: This disclosure is directed to systems and methods that automatically detects static content within a media item. While consuming a media item, such as a movie, a user might notice, unexpectedly, that a portion of the movie does not change resulting in a poor user experience. By dividing the media item into portions and analyzing the portions, the systems and methods described can automatically detect the static content and, in some instances, correct the static content.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated generation of textual descriptions of video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment including a first set of frames and first audio content, determining, using a first neural network, a first action that occurs in the first set of frames, and determining a first sound present in the first audio content. Some methods may include generating a vector representing the first action and the first sound, and generating, using a second neural network and the vector, a first textual description of the first segment, where the first textual description includes words that describe events of the first segment.