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 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: 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: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

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: 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: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.