Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for editing images using a web-based intermediary between a user interface on a client device and an image editing neural network(s) (e.g., a generative adversarial network) on a server(s). The present image editing system supports multiple users in the same software container, advanced concurrency of projection and transformation of the same image, clubbing transformation requests from several users hosted in the same software container, and smooth display updates during a progressive projection.

Abstract: A monitoring service monitors performance of an authentication application that authenticates a user or service and securely communicates a status of the authentication application to a service application providing a software service. The monitoring service generates a token using a private key. The token is stored in a secure datastore writable only by the monitoring service and is also provided to the service application. The service application validates a signature of the token using a public key and determines an authenticity of the token by comparing the received token to the stored token in the secure datastore. In doing so, there is a high degree of confidence that the token, or an associated encrypted message, originated from the monitoring service and properly identifies the status of the authentication application.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating partially textured accessible images. In one or more embodiments, the disclosed systems generate or access a color texture map specific to a given color vision deficiency. For example, the disclosed systems generate a color texture map that divides a color space into one or more textured segment of colors and one or more complementary untextured segments of colors. The disclosed systems can utilize the color texture map to intelligently apply different textures to subsets of pixels that contribute to color vision deficiency confusion. For example, the disclosed system maps pixels from a color image to the color texture map to identify a first subset of pixels corresponding to the textured segment of colors. The disclosed system generates a partially textured accessible image by applying a texture to the first subset of pixels.

Abstract: Systems and techniques for automatic digital parameter adjustment are described that leverage insights learned from an image set to automatically predict parameter values for an input item of digital visual content. To do so, the automatic digital parameter adjustment techniques described herein captures visual and contextual features of digital visual content to determine balanced visual output in a range of visual scenes and settings. The visual and contextual features of digital visual content are used to train a parameter adjustment model through machine learning techniques that captures feature patterns and interactions. The parameter adjustment model exploits these feature interactions to determine visually pleasing parameter values for an input item of digital visual content. The predicted parameter values are output, allowing further adjustment to the parameter values.

Abstract: In implementations of augmented reality systems for comparing physical objects, a computing device implements a comparison system to detect physical objects and physical markers depicted in frames of a digital video captured using an image capture device and displayed in a user interface. The comparison system associates a physical object of the physical objects with a physical marker of the physical markers based on an association distance estimated using two-dimensional coordinates of the user interface corresponding to a center of the physical object and a distance from the image capture device to the physical marker. Characteristics of the physical object are determined that are not displayed in the user interface based on an identifier of the physical marker. The comparison system generates a virtual object for display in the user interface that includes indications of a subset of the characteristics of the physical object.

Abstract: Systems and techniques for generating responsive documents are described. Digital content is organized into a structure that defines how content is presented when a document is displayed by a computing device. To generate the responsive document, relationships are defined among different digital content objects, such as groups of content objects to be presented together and content objects that are to be presented as alternatives of one another. Responsive patterns are assigned to grouped content objects, where each responsive pattern defines different layout configurations for displaying grouped content objects based on computing device display characteristics. In some implementations, multiple responsive patterns are assigned to a single content group and individual responsive patterns are associated with activation ranges for display characteristics that activate the responsive pattern.

Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize an optimization model for generating vector patterns with complex vector structures. For example, the disclosed systems iteratively optimize the similarity between local input and output neighborhoods that account for clusters. Specifically, based on an input exemplar vector image, the disclosed systems generate a sample input cluster representation for more robust iterative sample optimization and pattern reconstruction. To illustrate, the disclosed systems optimize output cluster configurations based on input clusters such that the output clusters minimize a shape energy and a link energy (e.g., to better preserve shape and structure details from the original vector pattern in the input exemplar vector image). From the output clusters, the disclosed systems can reconstruct additional vector elements to create a new vector image with a synthetic vector pattern.

Abstract: Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e.

Abstract: Embodiments are disclosed for removing typographic rivers from electronic documents. The method may include receiving an electronic document including a plurality of words for automatic typographic correction. A typographic river is identified in the electronic document, the typographic river including a plurality of nodes, each node including an empty glyph. A candidate adjustment that removes the first node of the plurality of nodes is identified and the candidate adjustment is applied to the electronic document.

Abstract: Embodiments are disclosed for identifying and generating symmetrical repeat edits to similar objects in an image. A selection of a first object and an edit to the first object in an image is received. The image is searched for a plurality of candidate objects that have a similar shape to the first object and the plurality of candidate objects are filtered to include one or more objects that are symmetrical with the first object. A symmetric object is selected from the plurality of candidate objects. An axis of symmetry is computed between the symmetric object and the first object. The edit is applied to the symmetric object and to the first object.

Abstract: Embodiments are disclosed for blending complex objects. The method may include identifying a first complex object and a second complex object. A first primary object associated with the first complex object and a first sequence of geometric repeat operations are determined. A second primary object associated with the second complex object and second sequence of geometric repeat operations are also determined. A blending operation is applied to the first primary object and the second primary object to generate one or more intermediate primary objects. One or more intermediate complex objects are generated from the one or more intermediate primary objects.

Abstract: In implementations of systems for visual reordering of partial vector objects, a computing device implements an order system to receive input data describing a region specified relative to a group of vector objects that includes a portion of a first vector object and a portion of second vector object. A visual order as between the portion of the first vector object and the portion of the second vector object within the region is determined. The order system computes a modified visual order as between the portion of the first vector object and the portion of the second vector object within the region based on the visual order. The order system generates the group of vector objects for display in a user interface using a render surface and a sentinel value to render pixels within the region in the modified visual order.

Abstract: Methods for modifying audio data include operations for accessing audio data having a first prosody, receiving a target prosody differing from the first prosody, and computing acoustic features representing samples. Computing respective acoustic features for a sample includes computing a pitch feature as a quantized pitch value of the sample by assigning a pitch value, of the target prosody or the audio data, to at least one of a set of pitch bins having equal widths in cents. Computing the respective acoustic features further includes computing a periodicity feature from the audio data. The respective acoustic features for the sample include the pitch feature, the periodicity feature, and other acoustic features. A neural vocoder is applied to the acoustic features to pitch-shift and time-stretch the audio data from the first prosody toward the target prosody.

Abstract: The present disclosure describes systems and methods for information retrieval. Embodiments of the disclosure provide a color embedding network trained using machine learning techniques to generate embedded color representations for color terms included in a text search query. For example, techniques described herein are used to represent color text in a same space as color embeddings (e.g., an embedding space created by determining a histogram of LAB based colors in a three-dimensional (3D) space). Further, techniques are described for indexing color palettes for all the searchable images in the search space. Accordingly, color terms in a text query are directly converted into a color palette and an image search system can return one or more search images with corresponding color palettes that are relevant to (e.g., within a threshold distance from) the color palette of the text query.

Abstract: In implementations of repeat object blending, a computing device implements a repeat object blending system, which is implemented to receive a digital image depicting a first object and a second object, where the first object is depicted as multiple instances of a repeated base object, and the second object is depicted as multiple instances of a visually different repeated base object. The repeat object blending system can identify visual characteristics of the first object and the second object. The repeat object blending system can then generate an intermediate object by blending one or more of the visual characteristics of the first object and one or more of the visual characteristics of the second object. The resulting intermediate object is a visual representation of the repeated base object blended with the visually different repeated base object.

Abstract: Systems and methods for image searching are described. The systems and methods include receiving a search query comprising user input for a reference image; converting the user input for the reference image to a preference statement using a machine learning model; encoding the preference statement in an embedding space to obtain an encoded preference statement; combining the encoded preference statement with an encoded reference image representing the reference image in the embedding space to obtain a multi-modal search encoding; and performing a search operation using the multi-modal search encoding to retrieve a second image, wherein the second image differs from the reference image based on the user input for the reference image.

Abstract: Systems and methods seamlessly blend edited and unedited regions of an image. A computing system crops an input image around a region to be edited. The system applies an affine transformation to rotate the cropped input image. The system provides the rotated cropped input image as input to a machine learning model to generate a latent space representation of the rotated cropped input image. The system edits the latent space representation and provides the edited latent space representation to a generator neural network to generate a generated edited image. The system applies an inverse affine transformation to rotate the generated edited image and aligns an identified segment of the rotated generated edited image with an identified corresponding segment of the input image to produce an aligned rotated generated edited image. The system blends the aligned rotated generated edited image with the input image to generate an edited output image.

Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

Abstract: Techniques are provided for scheduling multiple jobs on one or more cloud computing instances, which provide the ability to select a job for execution from among a plurality of jobs, and to further select a designated instance from among a plurality of cloud computing instances for executing the selected job. The job and the designated instance are each selected based on a probability distribution that a cost of executing the job on the designated instance does not exceed the budget. The probability distribution is based on several factors including a cost of prior executions of other jobs on the designated instance and a utility function that represents a value associated with a progress of each job. By scheduling select jobs on discounted cloud computing instances, the aggregate utility of the jobs can be maximized or otherwise improved for a given budget.