Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).

Abstract: Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).