Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly establishing compatibility connections between successive digital content editing operations in digital content operation pipelines or sequences. In particular, the disclosed systems can determine operation-specific features for executing digital content editing operations within a sequence. For example, the disclosed systems can determine digital image file types (or other digital content operation features) supported by particular digital content editing operations. Additionally, the disclosed systems can select a supported digital content operation feature for performing a particular operation to pass digital content from the operation to the next operation in a supported format, while also preserving quality and information richness.

Abstract: In implementations of systems for generating and propagating personal masking edits, a computing device implements a mask system to detect a face of a person depicted in a digital image displayed in a user interface of an application for editing digital content. The mask system determines an identifier for the person based on an identifier for the face. Edit data is received describing properties of an editing operation and a type of mask used to modify a particular portion of the person depicted in the digital image. The mask system edits an additional digital image identified based on the identifier of the person using the type of mask and the properties of the editing operation to modify the particular portion of the person as depicted in the additional digital image.

Abstract: Location operation conflict resolution techniques are described. In these techniques, a likely user's intent is inferred by a digital image editing system to prioritize anchor points that are to be a subject of a location operation. In an example in which multiple anchor points qualify for location operations at a same time, these techniques are usable to resolve conflicts between the anchor points based on an assigned priority. In an implementation, the priority is based on selection input location with respect to an object.

Abstract: Embodiments are disclosed for performing 3-D vectorization. The method includes obtaining a three-dimensional rendered image and a camera position. The method further includes obtaining a triangle mesh representing the three-dimensional rendered image. The method further involves creating a reduced triangle mesh by removing one or more triangles from the triangle mesh. The method further involves subdividing each triangle of the reduced triangle mesh into one or more subdivided triangles. The method further involves performing a mapping of each pixel of the three-dimensional rendered image to the reduced triangle mesh. The method further involves assigning a color value to each vertex of the reduced triangle mesh. The method further involves sorting each triangle of the reduced triangle mesh using a depth value of each triangle. The method further involves generating a two-dimensional triangle mesh using the sorted triangles of the reduced triangle mesh.

Abstract: Embodiments are disclosed for managing text co-editing in a conflict-free replicated data type (CRDT) environment. A method of co-editing management includes detecting a burst operation to be performed on a sequential data structure being edited by one or more client devices. A segment of the sequential data structure associated with the burst operation is determined based on a logical index associated with the burst operation. A tree structure associated with the segment is generated, where a root node of the tree structure corresponds to the burst operation. A global index for the root node of the tree structure is determined and an update corresponding to the burst operation, including the root node and the global index, is sent to the one or more client devices.

Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

Abstract: Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.

Abstract: Embodiments relate to the autonomous remediation of IT asset records in accordance with rules-based asset management policies. An IT asset record associated with an IT asset and having a first piece of IT asset information is received and evaluated for compliance in accordance with defined asset management rules. The asset management rules require the IT asset record to include both the first piece and a second piece of IT asset information. In view of the rules, a determination is made that the IT asset record requires remediation. Based on a determination that the IT asset record requires remediation, a decision-tree machine learning model is utilized to infer, based on IT asset information available in the IT asset record, at least the second piece of IT asset information to place the IT asset record in compliance with the asset management rules.

Abstract: Techniques are provided herein for retrieving images that correspond to a target subject matter within a target context. Although useful in a number of applications, the techniques provided herein are particularly useful in contextual product association and visualization. A method is provided to apply product images to a neural network. The neural network is configured to classify the products in the images. The images are associated with a context representing the combination of classified products in the images. These techniques leverage both seller-provided images of products and user-generated content, which potentially includes hundreds or thousands of images of the same or similar products as the seller-provided images. A graphical user interface is configured to permit a user to select the context of interest in which to visualize the products.

Abstract: The present technology provides for facilitating removal of undesired search results. In one embodiment, a search request including a search term(s) to use for performing a search is obtained. Thereafter, a search query is generated to execute the search. The search query includes the search terms and a removal parameter indicating a particular search result to exclude from search results returned in response to the search request. A set of search results are provided for display via a user device. Such a set of search results can be identified based on execution of the search query and exclude the particular search result.

Abstract: The technology described herein is directed to a cross-domain training framework that iteratively trains a domain adaptive refinement agent to refine low quality real-world image acquisition data, e.g., depth maps, when accompanied by corresponding conditional data from other modalities, such as the underlying images or video from which the image acquisition data is computed. The cross-domain training framework includes a shared cross-domain encoder and two conditional decoder branch networks, e.g., a synthetic conditional depth prediction branch network and a real conditional depth prediction branch network. The shared cross-domain encoder converts synthetic and real-world image acquisition data into synthetic and real compact feature representations, respectively.

Abstract: Digital content operation testing techniques are described. An authoring environment supports edit operations to digital content. The authoring environment includes an option to initiate testing of operation of edited digital content at a publish environment of a content delivery network, at which, the digital content is to be deployed. Data describing results of the testing is then communicated over the network based to the digital content editing system. The data is output within the user interface of the authoring environment in this example such that an effect of edits made to the digital content are viewable non-modally within the authoring environment.

Abstract: A method includes receiving a user event associated with content of an add-on for a web application displayed on a first user interface. The add-on is a non-native application executed using a hypertext markup language (HTML) element. The method further includes passing the user event to a document object model of the web application using a blank native element. The blank native element links the add-on to the document object model. The method further includes processing the user event using an HTML element renderer. The method further includes displaying updated content associated with the add-on based on the processed user event.

Abstract: Digital image object anchor point techniques are described that increase user efficiency in interacting with a user interface to create digital images. This is achieved through use of anchor points by the digital image editing system that are defined with respect to an actual geometry of the object. Further, filtering and prioritization techniques are also leveraged to promote real world utility and efficiency of these techniques as a balance between having too many and two few anchor points.

Abstract: Embodiments of the technology described herein are directed to a persona-specific navigation interface for a document. Initially, a user may select a persona associated with a document through a document navigation interface. A machine-learning model may identify an interest within a portion of the document. The interest may be mapped to the persona. A navigation interface that includes a navigable link to the portion of the document is generated and output for display. A user interaction with the navigable link is received. In response to the interaction, the portion of the document corresponding to the navigable link is output for display.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

Abstract: Aspects and features of the present disclosure provide a direct ray tracing operator with a low memory footprint for surfaces enriched with displacement maps. A graphics editing application can be used to manipulate displayed representations of a 3D object that include surfaces with displacement textures. The application creates an independent map of a displaced surface. The application ray-traces bounding volumes on the fly and uses the intersection of a query ray with a bounding volume to produce rendering information for a displaced surface. The rendering information can be used to generate displaced surfaces for various base surfaces without significant re-computation so that updated images can be rendered quickly, in real time or near real time.

Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.