Adobe, Inc.

Ramesh P B

Abhinav Darbari

Michael Iampietro

Ram Narain Yadav

Arvinder Singh

Anuj Dhawan

Chetan Nanda

Komal Kumar Desai

Sweta Agrawal

Steven Johnson

Manish Prasad

Laura Williams Argilla

Yogita Bhalla

Meagan Keane

Bronwyn Lewis

An analytics interface is provided for a content creation tool. Based on input to the analytics interface, first and second media content items are selected, where certain content is shared between the media content items. The system modifies first and second media content items to comply with formatting constraints of first and second social media services, respectively. The system associates, via a mapping, the first and second media content items with a group and publishes the first and second media content items to the first and second social media services, respectively. The system uses the mapping to match the group with first and second analytics respectively received from the first and second social media services about the first and second media content items. The system updates the analytics interface with visual representations of grouped analytics, which are generated based on matching the group with the first second analytics.

Combined interaction monitoring for media content groups on social media services

Disclosed systems and methods use machine-learning techniques to determine a set of parameters that if applied to a target video, apply a color characteristic of a reference video to the target video. For example, a color consistency application executing on a computing device computes a feature vector including a representation of a reference video and a target video. The application determines a set of color parameters (e.g., exposure, color temperature, tint, etc.) by applying the feature vector to one or more predictive models trained to determine color consistency. The application generates a preview image by applying the parameters to the target video. The applying causes an adjustment of exposure, color temperature, or tint in the target video such that a color consistency of the adjusted target video is consistent with a color consistency of the reference video. The color consistency application provides settings to further adjust the parameters.

Machine learning techniques for increasing color consistency across videos

Rendering content in a graphical user interface (GUI) is disclosed. A region of a GUI is defined as a graphical object. The graphical object corresponds to content. Input is received from an input device. The received input identifies a location on the GUI. The location is in spatial proximity to the defined region of a graphical object. In response to receiving such input, the content corresponding to the graphical object is rendered in the defined region of the GUI. The content may be rendered only in a subset of the defined region of the GUI. Further input, such as input corresponding to a particular location on the GUI, or input corresponding to a change in locations on the GUI in a particular direction, may be received, causing one or more commands to be executed on the rendered content.

Rendering of content in a defined region of a graphical user interface

Systems and methods for identifying, tracking, and using objects in a video or similar electronic content, including methods for tracking one or more moving objects in a video. This can involve tracking one or more feature points within a video scene and separating those feature points into multiple layers based on motion paths. Each such motion layer can be further divided into different clusters, for example, based on distances between points. These clusters can then be used as an estimate to define the boundaries of the objects in video. Objects can also be compared with one another in cases in which identified objects should be combined and considered a single object. For example, if two objects in the first two frames have significantly overlapping areas, they may be considered the same object. Objects in each frame can further be compared to determine the life of the objects across the frames.

Systems and methods of tracking objects in video

Generating smart tags that allow a user to locate any portion of image content without viewing the image content is disclosed. Image-based processing is performed on image content to find an event of interest that is an occurrence captured by the image content. Thus, metadata is derived from analyzing the image content. The metadata is then analyzed. Different types of characteristics associated with portions of the image content as indicated by the metadata are detected. Responsive to this, tags are created, and different types of tags are applied to the portions of image content to categorize the portions into classes. Thus, a tag is associated with each portion of the image content including the event of interest. The tag describes a characteristic of that portion of the image content. Display of the different types of tags is initiated for selective viewing of the portions of the image content.

Generation of smart tags to locate elements of content

Methods and apparatus provide for an output manager that receives an identification of content. The output manager receives an identification of a set of filters to be applied to the content where each filter in the set of filters defines a sequence of steps to be applied to the content. The output manager identifies shared steps that are common to the sequence of steps of at least two filters in the set of filters. Thus, upon a first execution of a shared step in a sequence of steps by a filter, the output manager caches output of the first execution to be used in place of execution of that shared step in a sequence of steps of another filter in the set of filters.

Real time auto-tagging system

Embodiments herein include presenting smart tags describing characteristics of image content in a hierarchy, and performing operations on the hierarchy to find particular image content within a larger amount of image content. Image content and corresponding tags are maintained. The corresponding tags associated with the image content are presented in a hierarchy. Each tag type in the hierarchy represents a characteristic associated with the image content. Each tag in the hierarchy is derived based on image-based processing applied to the image content. In response to receiving a selection of at least one tag in the hierarchy, display of the image content associated with the at least one tag is initiated. A user is able to quickly and easily find desired image content by using the hierarchy to look at tags, select a type of tag from the hierarchy, and thereafter view any content tagged with the selected tag type.

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Uttar Pradesh, India

Ramesh P B