Abstract: Systems and methods for information retrieval are described. Embodiments generate a dense embedding for each of a plurality of media objects to be searched, generate a sparse embedding for each of the media objects using an encoder that takes the dense embedding as an input, wherein the sparse embedding satisfies a sparsity constraint that is applied to at least one layer of the encoder during training, and perform a search on the plurality of media objects based at least in part on the sparse embedding.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image depicting an object; generate a sequence of tokens including a set of tokens corresponding to the object and a set of mask tokens corresponding to an additional object to be inserted into the image; generate a placement token value for the set of mask tokens based on the sequence of tokens using a sequence encoder, wherein the placement token value represents position information of the additional object; and insert the additional object into the image based on the position information to obtain a composite image.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.

Abstract: The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.

Abstract: Systems and methods for information retrieval are described. Embodiments generate a dense embedding for each of a plurality of media objects to be searched, generate a sparse embedding for each of the media objects using an encoder that takes the dense embedding as an input, wherein the sparse embedding satisfies a sparsity constraint that is applied to at least one layer of the encoder during training, and perform a search on the plurality of media objects based at least in part on the sparse embedding.

Abstract: The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.

Abstract: A mobile wireless communication device, in response to a circuit switched page message, suspends an LTE session and engages in a circuit switched call without a determination of receipt of a connection release message from the LTE network. The mobile wireless communication device, therefore, suspends the LTE session before, or without, receiving any connection release message authorizing the termination of the LTE session.