Abstract: A system for creating accessibility enhanced content includes a computing platform having processing hardware and a system memory storing a software code and a machine learning (ML) model, the software code providing a graphical user interface (GUI). The processing hardware executes the software code to identify a user of the system, obtain a user profile of the user, obtain, from one or more application(s) utilized by the user, activity data relating to use of the application(s) by the user, and modify, using the user profile and the activity data, one or more node weights of the ML model to provide a tuned ML model. The processing hardware further executes the software code to infer, using the tuned ML model, at least one action for advancing a development of the user and output to the user, using the UI, a recommendation for performing the at least one action by the user.

Abstract: A system for assessing performer density includes a computing platform having processing hardware and a memory storing software code. The processing hardware is configured to execute the software code to receive content and content data, the content data identifying one or more performer(s) depicted or referenced in the content, and determine, using the content and the content data, one or more segment(s) of the content in which the performer(s) is/are depicted or referenced. The processing hardware is further configured to execute the software code to infer, for each determined segment(s) of the content, a respective importance of the performer(s) in a respective context of each determined segment(s), and calculate, based on the determined segment(s) of the content and the respective importance of the performer(s), a respective density score of each performer with respect to the content.

Abstract: According to one implementation, a system includes a computing platform having processing hardware, a system memory storing a software code; and a machine learning model based classifier. The processing hardware is configured to execute the software code to receive tagging quality assurance (QA) data including multiple terms applied as tags and corrections to those tags, to identify, using the tagging QA data, a first problematic term, and to classify, using the machine learning model based classifier, the first problematic term as one of confusing or flawed. The processing hardware is further configured to execute the software code to obtain, when the first problematic term is classified as confusing, a comparative sample for clarifying use of the first problematic term, and to obtain, when the first problematic term is classified as flawed, modification data for editing a predetermined annotation taxonomy including the first problematic term.

Abstract: A system includes a computing platform having processing hardware, and a systems memory storing a software code. The processing hardware is configured to execute the software code to receive content including an image having multiple image regions, determine boundaries of each of the image regions to identify multiple bounded image regions, identify, within each of the bounded image regions, one or more local features and one or more global features, and identify, within each of the hounded image regions, another one or more local features based on a comparison with corresponding local features identified in each of one or more other bounded image regions. The processing hardware is further configured to execute the software code to annotate each of the bounded image regions using its respective one or more local features, its other one or more local features, and its one or more global features, to provide annotated content.

Abstract: A system includes a computing platform having processing hardware, and a memory storing software code. The processing hardware is configured to execute the software code to receive an image having a plurality of image regions, determine a boundary of each of the image regions to identify a plurality of bounded image regions, and identify, within each of the bounded image regions, one or more image sub-regions to identify a plurality of image sub-regions. The processing hardware is further configured to execute the software code to identify, within each of the bounded image regions, one or more first features, respectively, identify, within each of the image sub-regions, one or more second features, respectively, and provided an annotated image by annotating each of the bounded image regions using the respective first features and annotating each of the image sub-regions using the respective second features.

Abstract: A system for authenticating digital contents includes a computing platform having a hardware processor and a memory storing a software code. According to one implementation, the hardware processor executes the software code to receive digital content, identify an image of a person depicted in the digital content, determine an ear shape parameter of the person depicted in the image, determine another biometric parameter of the person depicted in the image, and calculate a ratio of the ear shape parameter of the person depicted in the image to the biometric parameter of the person depicted in the image. The hardware processor is also configured to execute the software code to perform a comparison of the calculated ratio with a predetermined value, and determine whether the person depicted in the image is an authentic depiction of the person based on the comparison of the calculated ratio with the predetermined value.

Abstract: Techniques for machine learning optimization are provided. A video comprising a plurality of segments is received, and a first segment of the plurality of segments is processed with a machine learning (ML) model to generate a plurality of tags, where each of the plurality of tags indicates presence of an element in the first segment. A respective accuracy value is determined for each respective tag of the plurality of tags, where the respective accuracy value is based at least in part on a maturity score for the ML model. The first segment is classified as accurate, based on determining that an aggregate accuracy of tags corresponding to the first segment exceeds a predefined threshold. Upon classifying the first segment as accurate, the first segment is bypassed during a review process.

Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

Abstract: According to one implementation, a system includes a computing platform having processing hardware, a system memory storing a software code; and a machine learning model based classifier. The processing hardware is configured to execute the software code to receive tagging quality assurance (QA) data including multiple terms applied as tags and corrections to those tags, to identify, using the tagging QA data, a first problematic term, and to classify, using the machine learning model based classifier, the first problematic term as one of confusing or flawed. The processing hardware is further configured to execute the software code to obtain, when the first problematic term is classified as confusing, a comparative sample for clarifying use of the first problematic term, and to obtain, when the first problematic term is classified as flawed, modification data for editing a predetermined annotation taxonomy including the first problematic term.

Abstract: According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Abstract: According to one implementation, an automated audio mapping system includes a computing platform having a hardware processor and a system memory storing an audio mapping software code including an artificial neural network (ANN) trained to identify multiple different audio content types. The hardware processor is configured to execute the audio mapping software code to receive content including multiple audio tracks, and to identify, without using the ANN, a first music track and a second music track of the multiple audio tracks. The hardware processor is further configured to execute the audio mapping software code to identify, using the ANN, the audio content type of each of the multiple audio tracks except the first music track and the second music track, and to output a mapped content file including the multiple audio tracks each assigned to a respective one predetermined audio channel based on its identified audio content type.

Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

Abstract: A system for authenticating digital contents includes a computing platform having a hardware processor and a memory storing a software code. According to one implementation, the hardware processor executes the software code to receive digital content, identify an image of a person depicted in the digital content, determine an ear shape parameter of the person depicted in the image, determine another biometric parameter of the person depicted in the image, and calculate a ratio of the ear shape parameter of the person depicted in the image to the biometric parameter of the person depicted in the image. The hardware processor is also configured to execute the software code to perform a comparison of the calculated ratio with a predetermined value, and determine whether the person depicted in the image is an authentic depiction of the person based on the comparison of the calculated ratio with the predetermined value.

Abstract: A recommendation engine generates contextually relevant media programs tailored to the specific context of the user. The recommendation engine interfaces with one or more Internet-of-Things (IoT) appliances to determine one or more food items stored within the IoT appliance(s) and/or one or more cooking capabilities associated with the IoT appliance(s). The recommendation engine also gathers profile data that reflects a set of preferences associated with the user. Based on the gathered data, the recommendation engine queries a database of cooking program data associated with various cooking programs. The recommendation engine obtains cooking program data that is contextually relevant to the user and then generates a contextual cooking program that describes how to perform a given recipe. During playback of the contextual cooking program, the recommendation engine can modify the contextual cooking program based on the progress of the user in following the recipe.

Abstract: A system for authenticating digital contents includes a computing platform having a hardware processor and a memory storing a software code. According to one implementation, the hardware processor executes the software code to receive digital content, identify an image of a person depicted in the digital content, determine an ear shape parameter of the person depicted in the image, determine another biometric parameter of the person depicted in the image, and calculate a ratio of the ear shape parameter of the person depicted in the image to the biometric parameter of the person depicted in the image. The hardware processor is also configured to execute the software code to perform a comparison of the calculated ratio with a predetermined value, and determine whether the person depicted in the image is an authentic depiction of the person based on the comparison of the calculated ratio with the predetermined value.

Abstract: A system for securing a content processing pipeline includes a computing platform having a hardware processor and a memory storing a software code. The hardware processor executes the software code to insert a synthesized test image configured to activate one or more neurons of a malicious neural network into a content stream, provide the content stream as an input stream to a first processing node of the pipeline, and receive an output stream including a post-processed test image. The hardware processor further executes the software code to compare the post-processed test image in the output with an expected image corresponding to the synthesized test image, and to validate at least one portion of the pipeline as secure when the post-processed test image in the output matches the expected image.

Abstract: According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

Abstract: Various embodiments of the invention disclosed herein provide techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline. A production asset management system transmits, to an editorial workstation, a first content library that includes first metadata associated with a 3D production asset that is included in an editorial cut. The first metadata allows a user to see what versions of the 3D production asset are available for incorporation into the editorial cut. The production asset management system retrieves second metadata associated with a second version of the 3D production asset from a production database. The production asset management system retrieves, based on the second metadata, the second version of the 3D production asset from the production database. The production asset management system transmits the second version of the 3D production asset to the editorial workstation for incorporation into the editorial cut.

Abstract: According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

Abstract: A device for use in a system for tracking a chain-of-manipulation (CoM) of audio-video (AV) content includes a hardware processor, and a system memory storing a CoM tagging software code and an AV processing application. The hardware processor executes the CoM tagging software code to detect manipulation of the AV content by the AV processing application that modifies at least one of video or audio of the AV content, and to generate, when the manipulation is detected, one or more CoM tag(s) including a device identification tag identifying the device used for modifying the AV content. The hardware processor also executes the CoM tagging software code to insert the CoM tag(s) in each modified segment of the AV content modified by the AV processing application, the inserted CoM tag(s) being human and machine imperceptible when the AV content is played back by a playback device on a display.