Abstract: An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.

Abstract: Systems and methods are disclosed for reflowing documents to display semantically related content. Embodiments may include receiving a request to view a document that includes body text and one or more images. A trimodal document relationship model identifies relationships between segments of the body text and the one or more images. A linearized view of the document is generated based on the relationships and the linearized view is caused to be displayed on a user device.

Abstract: In accordance with the described techniques, a background generation system receives one or more images depicting an object, and textual information describing the object. A generative text model is employed to generate a prompt based on the one or more images and the textual information. Further, a generative image model is employed to generate an output image. To do so, the generative image model generates a background image based on the prompt, and the object is incorporated into the background image. Using a visual saliency model, the background generation system determines a visual saliency defining a degree of fixation on the object within the output image. The background generation system outputs the output image based on the visual saliency meeting a threshold.

Abstract: Techniques for accessibility-enabled application switching are provided. In an example method, a processing device receives a status indication of a screen reader browsing content using a first application. The processing device receives a context switch indication from the screen reader, including a designation of a second application as a target application based on context switch accessibility code. The processing device generates a token comprising a client device identifier and identifiers of the source and target applications. The processing device then receives a status indication of the screen reader browsing content using the second application and a second context switch indication. The processing device accesses the token based on the client device identifier and determines the source application. The processing device updates the identifiers of the source and target applications.

Abstract: Techniques are disclosed for generating image recommendations to facilitate the sale of a product. An example methodology includes identifying a product category associated with an image of the product provided by the seller, and a product sub-category associated with the product image. The method further includes retrieving one or more images of for-sale items. The retrieval is based on a search of for-sale listings using the identified product category and the identified product sub-category. The method further includes clustering the retrieved images of for-sale items into groups, each group associated with a perspective viewpoint of the for-sale item. The method further includes providing a selected image from each group as an image recommendation. The selection is based on a value score associated with each of the images of the for-sale items. A graphical status indicating completeness of the seller's image set is updated in response to recommended images being adopted.

Abstract: A digital content management system may include a set of one or more memory components and a set of one or more processing devices coupled to the set of one or more memory components. The set of one or more processing devices to perform operations comprising: obtaining one or more electronic signatures on a digital document to yield an electronically signed digital document, storing the electronically signed digital document at an off-chain address, using a trained machine learning model to create a smart contract, the smart contract comprising a reference to the off-chain address; and deploying the smart contract to a blockchain.

Abstract: Methods and systems provide a human kidney perfusion model with dual blood supply channels, permitting two kidneys from the same donor to be pumped simultaneously for rigorously controlled therapeutic testing.

Abstract: Methods and systems provide a human kidney perfusion model with dual blood supply channels, permitting two kidneys from the same donor to be pumped simultaneously for rigorously controlled therapeutic testing.

Abstract: Methods and systems for mapping video conferencing content to video frames are provided. In an example method, a processing device receives video conference information and a digital video, the digital video including a plurality of frames. The processing device segments the video conference information into one or more video-conference time segments and the digital video into one or more digital-video time segments. The processing device associates each video-conference time segment with a digital-video time segment. The processing device maps first content information of a first video-conference time segment of the one or more video-conference time segments onto a first digital-video time segment associated with the first video-conference time segment based a first identifier of the first content information. The processing device causes the first content information to be displayed during a displaying of the digital video.

Abstract: Systems and methods for predictively and dynamically updating content using nested A/B testing are provided. In certain embodiments, a system may assign a content item to a dynamic content portion of a communication. The communication may be sent to a first plurality of recipients. The system may receive information about interactions of a second plurality of recipients with the communication, wherein the second plurality of recipients is a subset of the first plurality of recipients. The system can determine a first conversion metric. The system can then compare the first conversion metric to a conversion metric threshold and based on the comparison, modify the dynamic content portion of the communication to generate a modified communication. The modified communication may be sent to a third plurality of recipients, wherein the third plurality of recipients is a subset of the first plurality of recipients not including the second plurality of recipients.

Abstract: Certain aspects and features of this disclosure relate to automatic generation of composite images. For example, a method involves producing a representative image corresponding to a composite image based on a presentation context of input objects and segmenting the generated objects from the representative image to extract the generated objects from the representative image. The method also includes generating an inferred disposition of each of the generated objects and transforming each of the input objects to the inferred disposition of a corresponding generated object. The method can also include transmitting, storing, display, or rendering, in response to the transforming, the composite image of the input objects. Certain aspects and features also include computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

Abstract: An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.

Abstract: Certain aspects and features of the present disclosure relate to providing message generation based on communication loss correlation. For example, a method involves detecting a loss of a communication with a communication recipient and parsing content of the communication. The method further involves computing communication-based values for recipient intent attributes corresponding to the communication and action-based values for the recipient intent attributes corresponding to the communication. The method additionally involves correlating the communication-based values and the action-based values for the recipient intent attributes to determine common intent attributes corresponding to the communication recipient. The method also involves generating at least one message configured for the communication recipient based on the common intent attributes. The method can also involve transmitting the at least one message to a target device.

Abstract: Some embodiments described herein relate to systems and methods for parameter-based synthetic model generation and recommendations including an image generation module and a recommendation module. The image generation module can receive one or more parameters and, responsive to receive the one or more parameters, generate a parameterized image using a generative machine learning model. The generative ML model may use the parameters as a seed for generating the parameterized image. The recommendation module may generate a first set of recommendations for a user of the client device and receive the one or more parameters. The recommendation module may determine, based on the one or more parameters, a second set of recommendations for the user of the client device. The second set of recommendations may include at least one element from the first set of recommendations.

Abstract: An object affinity determination and scoring system is described that is configured to support control by object providers in locating related objects. In a first example, an affinity system supports generation of affinity rules through interaction with a rule generation user interface. In a second example, the affinity system supports training and retraining of a machine-learning model to generate the affinity score. In a third example, the affinity scoring module supports output of a user interface having an input portion that supports user interaction to determine an affinity of selected objects to each other.

Abstract: Systems and methods for product description augmentation are provided. According to one aspect, a method for product augmentation includes performing, by an attribute inference component, a semantic analysis of a product review for a product to obtain review data including an attribute of the product; computing, by a delta component, a difference between the review data and a product description for the product, wherein the difference includes the attribute; and generating, by a generative machine learning model, an augmented product description for the product based on the difference, wherein the augmented product description describes the attribute.

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: An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating a composite image comprising objects in positions from two or more different digital images. In one or more embodiments, the disclosed system receives a sequence of images and identifies objects within the sequence of images. In one example, the disclosed system determines a target position for a first object based on detecting user selection of the first object in the target position from a first image. The disclosed system can generate a fixed object image comprising the first object in the target position. The disclosed system can generate preview images comprising the fixed object image with the second object sequencing through a plurality of positions as seen in the sequence of images. Based on a second user selection of a desired preview image, the disclosed system can generate the composite image.

Abstract: Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.