Abstract:
Some embodiments provide a novel user interface (UI) tool that is a unified slider control, which includes multiple sliders that slide along a region. The region is a straight line in some embodiments, while it is an angular arc in other embodiments. In some embodiments, the unified slider control is used in a media editing application to allow a user to modify several different properties of the image by moving several different sliders along the region. Each slider is associated with a property of the image. A position of the slider in the region corresponds to a value of the property associated with the slider.
Abstract:
Some embodiments of the image editing and organizing application described herein provide a multi-stage automatic enhancement process. The process takes an input image and feeds it through multiple different enhancement operations. The multiple enhancement operations of some embodiments are carried out in a particular order. In some embodiments, the particular order starts with exposure adjustment, then a white balance adjustment, then a vibrancy adjustment, then a tonal response curve adjustment, then a shadow lift adjustment.
Abstract:
Techniques are disclosed to provide user control over the manipulation of a digital image. The disclosed techniques enable a user to apply various textures that mimic traditional artistic media to a selected image. User selection of a texture level results in the blending of texturized versions of the selected image in accordance with the selected texture level. User selection of a color level results in the adjustment of color properties of the selected image that are included in the output image. Control of the image selection, texture type selection, texture level selection, and color level selection may be provided through an intuitive graphical user interface.
Abstract:
Some embodiments provide a method for color balancing an image. The method receives a first selection of a first mode of a color balance tool that includes several different color balance modes. Each color balance mode is for applying color balance operations to the image. The method uses the first mode of the color balance tool to apply a first set of color balance operations to the image. The method receives a second selection to switch from the first mode to a second mode of the color balance tool. The method uses the second mode of the color balance tool to apply a second set of color balance operations to the image.
Abstract:
A method and apparatus for generating a grayscale image. The method and apparatus receive a single value. From the single value, the method and apparatus generate a set of grayscale weighting values. The method and apparatus generate the grayscale based on a color image and the set of grayscale weighting values. By limiting the number of values to a single value, the method and apparatus prevents a user from arbitrarily selecting a number of possible weighting values which could result in a grayscale image that is too dim or too bright. This single control method and apparatus quickly and efficiently produces a grayscale image that is neither too bright nor too dim.
Abstract:
A method is provided that includes predicting, using a language model, one or more words from a first set of words expected to be difficult for a reader, and providing the first set of words for display to the reader. The predicted one or more words in the first set of words are displayed differently from non-predicted words in the first set of words.
Abstract:
A method includes obtaining a speech proficiency value indicator indicative of a speech proficiency value associated with a user of the electronic device. The method further includes in response to determining that the speech proficiency value satisfies a threshold proficiency value: displaying training text via the display device; obtaining, from the audio sensor, speech data associated with the training text, wherein the speech data is characterized by the speech proficiency value; determining, using a speech classifier, one or more speech characterization vectors for the speech data based on linguistic features within the speech data; and adjusting one or more operational values of the speech classifier based on the one or more speech characterization vectors and the speech proficiency value.
Abstract:
A method includes obtaining user input interaction data. The user input interaction data includes one or more user interaction input values respectively obtained from the corresponding one or more input devices. The user input interaction data includes a word combination. The method includes generating a user interaction-style indicator value corresponding to the word combination in the user input interaction data. The user interaction-style indicator value is a function of the word combination and a portion of the one or more user interaction input values. The method includes determining, using a semantic text analyzer, a semantic assessment of the word combination in the user input interaction data based on the user interaction-style indicator value and a natural language assessment of the word combination. The method includes generating a response to the user input interaction data according to the user interaction-style indicator value and the semantic assessment of the word combination.
Abstract:
A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.
Abstract:
Some embodiments provide a novel user interface (UI) tool that is a unified slider control, which includes multiple sliders that slide along a region. The region is a straight line in some embodiments, while it is an angular arc in other embodiments. In some embodiments, the unified slider control is used in a media editing application to allow a user to modify several different properties of the image by moving several different sliders along the region. Each slider is associated with a property of the image. A position of the slider in the region corresponds to a value of the property associated with the slider.