Abstract: An image including a visual capture of a scene may be accessed. The image may be processed through a convolutional neural network. The convolutional neural network may generate a set of two-dimensional feature maps based on the image. The set of two-dimensional feature maps may be processed through a contextual long short-term memory unit. The contextual long short-term memory unit may generate a set of two-dimensional outputs based on the set of two-dimensional feature maps. A set of attention-masks for the image may be generated based on the set of two-dimensional outputs and the set of two-dimensional feature maps. The set of attention-masks may define dimensional portions of the image. The scene may be classified based on the two-dimensional outputs.

Abstract: Methods and apparatus for the generation of interpolated frames of video data. In one embodiment, the interpolated frames of video data are generated by obtaining two or more frames of video data from a video sequence; determining frame errors for the obtained two or more frames from the video sequence, determining whether the frame errors exceed a threshold value; performing a multi-pass operation; performing a single-pass operation; performing frame blending; performing edge correction; and generating the interpolated frame of image data.

Abstract: A system and/or method configured to determine highlight segments. Content files that define content in a content segment set may be obtained. A highlight segment set may be determined from the content segment set. Determining the highlight segment set may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the content segment set may be selected as a selected content segment for inclusion in the highlight segment set. At, (b) diversity scores for content segments that are (i) included in the content segment set and (ii) not yet selected for inclusion in the highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the highlight segment set for future iterations based on the diversity scores.

Abstract: Systems and method of generating video summaries are presented herein. Information defining a video may be obtained. The video may include a set of frame images. Parameter values for parameters of individual frame images of the video may be determined. Interest weights for the frame images may be determined. An interest curve for the video that characterizes the video by interest weights as a function of progress through the set of frame images may be generated. One or more curve attributes of the interest curve may be identified and one or more interest curve values of the interest curve that correspond to individual curve attributes may be determined. Interest curve values of the interest curve may be compared to threshold curve values. A subset of frame images of the video to include within a video summary of the video may be identified based on the comparison.

Abstract: A system and/or method configured to determine highlight segment sets. Content files that define content in content segment sets may be obtained. Individual highlight segment sets of content segments may be determined from the content segment sets. Determining a first highlight segment set of content segments included in the first content segment may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the first content segment set may be selected as a selected content segment for inclusion in the first highlight segment set. At, (b) diversity scores for content segments that are (i) included in the first content segment set and (ii) not yet selected for inclusion in the first highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the first highlight segment set for future iterations based on the diversity scores.

Abstract: Systems and method of generating video summaries are presented herein. Information defining a video may be obtained. The video may include a set of frame images. Parameter values for parameters of individual frame images of the video may be determined. Interest weights for the frame images may be determined. An interest curve for the video that characterizes the video by interest weights as a function of progress through the set of frame images may be generated. One or more curve attributes of the interest curve may be identified and one or more interest curve values of the interest curve that correspond to individual curve attributes may be determined. Interest curve values of the interest curve may be compared to threshold curve values. A subset of frame images of the video to include within a video summary of the video may be identified based on the comparison.

Abstract: Images may be extracted from a video. The images may be grouped into image groups. Numbers and types of classified visuals within the images may be detected. Individual types of classified visuals may correspond to individual classification weights. Image classification weights for the images may be determined based on the numbers and the types of classified visuals and the individual classification weights. Interest weights for the images may be determined based on the image classification weights and the sizes of the image groups to which the individual images belong. An interest curve may be generated based on the interest weights. A retime curve may be generated based on the interest curve. Time lapse images to be included in the time lapse video may be determined based on the images and the retime curve. The time lapse video may be generated based on the time lapse images.

Abstract: A first command may be received from a client computing platform. The first command may include a proxy image. The proxy image may represent an image stored on the client computing platform. An identifier may be associated with the proxy image. The identifier may be transmitted to the client computing platform. The identifier may be associated with the image stored on the client computing platform. Edits to the image based upon the proxy image may be determined at a remote computing platform. Instructions may be transmitted from the remote computing platform to the client computing platform. The instructions may include the identifier. The instructions may be configured to cause the client computing platform to process the edits on the image.