Abstract: In an example embodiment, a content agnostic approach to quality of assessment of social media posts or other items or content in an online network, such as a social networking service, is utilized. Specifically, information about members contained in member profiles may be used to derive meaningful insights about posts they interact with collectively.

Abstract: In an example embodiment, a content agnostic approach to quality of assessment of social media posts or other items or content in an online network, such as a social networking service, is utilized. Specifically, information about members contained in member profiles may be used to derive meaningful insights about posts they interact with collectively.

Abstract: In various example embodiments, a system and method for providing a visual example-based user interface for adjusting images is provided. In example embodiments, a new image to be adjusted is received. A plurality of basis styles is generated by applying adjustment parameters to the new image. Each of the plurality of basis styles comprises an adjusted version of the new image with an adjustment of at least one image control. A user interface is provided that positions a version of the new image in a center portion and positions the plurality of basis styles on the user interface based on the adjustment parameters applied to the new image. A control mechanism is provided over the version of the new image whereby movement of the control mechanism to a new position on the user interface causes the version of the new image to adjust accordingly.

Abstract: Techniques and systems are described herein that support improved object painting in digital images through use of perspectives and transfers in a digital medium environment. In one example, a user interacts with a two-dimensional digital image in a user interface output by a computing device to apply digital paint. The computing device fits a three-dimensional model to an object within the image, e.g., the face. The object, as fit to the three-dimensional model, is used to support output of a plurality of perspectives of a view of the object with which a user may interact to digitally paint the object. As part of this, digital paint as specified through the user inputs is applied directly by the computing device to a two-dimensional texture map of the object. This may support transfer of digital paint by a computing device between objects by transferring the digital paint using respective two-dimensional texture maps.

Abstract: In various example embodiments, a system and method for providing a visual example-based user interface for adjusting images is provided. In example embodiments, a new image to be adjusted is received. A plurality of basis styles is generated by applying adjustment parameters to the new image. Each of the plurality of basis styles comprises an adjusted version of the new image with an adjustment of at least one image control. A user interface is provided that positions a version of the new image in a center portion and positions the plurality of basis styles on the user interface based on the adjustment parameters applied to the new image. A control mechanism is provided over the version of the new image whereby movement of the control mechanism to a new position on the user interface causes the version of the new image to adjust accordingly.

Abstract: In various example embodiments, a system and method for providing a visual example-based user interface for adjusting images is provided. In example embodiments, a new image to be adjusted is received. A plurality of basis styles is generated by applying adjustment parameters to the new image. Each of the plurality of basis styles comprises an adjusted version of the new image with an adjustment of at least one image control. A user interface is provided that positions a version of the new image in a center portion and positions the plurality of basis styles on the user interface based on the adjustment parameters applied to the new image. A control mechanism is provided over the version of the new image whereby movement of the control mechanism to a new position on the user interface causes the version of the new image to adjust accordingly.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Techniques are disclosed for correcting facial sentiment of digital images. Facial data captured in a target image is analyzed to obtain facial-based sentiment. A favored sentiment is determined based at least in part on the facial-based sentiment. The favored sentiment is then applied to at least one face included in the target image that doesn't reflect the favored sentiment. Analyzing facial data may include detecting facial landmarks that are good indicators of sentiment (e.g., eyes, mouth, eyebrows, jawline, and nose). Such landmarks can be processed, with supervised machine learning, to detect the corresponding facial sentiment. A favored sentiment of the target image is thus identified, and can be applied to one or more non-compliant faces in the target image. In some embodiments, the favored sentiment can be further based on a plurality of additional sentiment indicators, including geo data, text, and/or other images associated with the target image.

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.

Abstract: Techniques and systems are described herein that support improved object painting in digital images through use of perspectives and transfers in a digital medium environment. In one example, a user interacts with a two-dimensional digital image in a user interface output by a computing device to apply digital paint. The computing device fits a three-dimensional model to an object within the image, e.g., the face. The object, as fit to the three-dimensional model, is used to support output of a plurality of perspectives of a view of the object with which a user may interact to digitally paint the object. As part of this, digital paint as specified through the user inputs is applied directly by the computing device to a two-dimensional texture map of the object. This may support transfer of digital paint by a computing device between objects by transferring the digital paint using respective two-dimensional texture maps.

Abstract: Facilitating text identification and editing in images in which in one or more embodiments, a user selection of a location in an area of text in an image is received. Given the location, a region of interest that includes text (including the location of the user selection) in the image is determined. Distortion resulting from a surface in the image on which the text is situated being at some angle other than parallel to the image capture plane is also corrected. One or more fonts and font sizes of the text in the region of interest are also detected. Various actions can be taken on the text in the region of interest (e.g., editing the text and/or identifying the text).

Abstract: Techniques are disclosed for correcting facial sentiment of digital images. Facial data captured in a target image is analyzed to obtain facial-based sentiment. A favored sentiment is determined based at least in part on the facial-based sentiment. The favored sentiment is then applied to at least one face included in the target image that doesn't reflect the favored sentiment. Analyzing facial data may include detecting facial landmarks that are good indicators of sentiment (e.g., eyes, mouth, eyebrows, jawline, and nose). Such landmarks can be processed, with supervised machine learning, to detect the corresponding facial sentiment. A favored sentiment of the target image is thus identified, and can be applied to one or more non-compliant faces in the target image. In some embodiments, the favored sentiment can be further based on a plurality of additional sentiment indicators, including geo data, text, and/or other images associated with the target image.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.

Abstract: Techniques are described for analyzing a video for memorability, identifying content features of the video that are likely to be memorable, and scoring specific content features within the video for memorability. The techniques can be optionally applied to selected features in the video, thus improving the memorability of the selected features. The features may be organic features of the originally captured video or add-in features provided using an editing tool. The memorability of video features, text features, or both can be improved by analyzing the effects of applying different styles or edits (e.g., sepia tone, image sharpen, image blur, annotation, addition of object) to the content features or to the video in general. Recommendations can then be provided regarding memorability score caused by application of the image styles to the video features.

Abstract: Facilitating text identification and editing in images in which in one or more embodiments, a user selection of a location in an area of text in an image is received. Given the location, a region of interest that includes text (including the location of the user selection) in the image is determined. Distortion resulting from a surface in the image on which the text is situated being at some angle other than parallel to the image capture plane is also corrected. One or more fonts and font sizes of the text in the region of interest are also detected. Various actions can be taken on the text in the region of interest (e.g., editing the text and/or identifying the text).

Abstract: Facilitating text identification and editing in images in which in one or more embodiments, a user selection of a location in an area of text in an image is received. Given the location, a region of interest that includes text (including the location of the user selection) in the image is determined. Distortion resulting from a surface in the image on which the text is situated being at some angle other than parallel to the image capture plane is also corrected. One or more fonts and font sizes of the text in the region of interest are also detected. Various actions can be taken on the text in the region of interest (e.g., editing the text and/or identifying the text).

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.