Abstract: Systems and methods for augmenting a data set are provided. An example method may include locating a foreground object disposed within a seed image, identifying an object class corresponding to the foreground object, and, based on the identified object class, determining a target value for an object property of the foreground object. The example method may also include applying a transformation function to transform the foreground object into a transformed object, where the transformation function modifies the object property of the foreground object from having an initial value to having the target value. The example method may further include transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.

Abstract: Systems and methods for augmenting a data set are provided. An example method may include locating a foreground object disposed within a seed image, identifying an object class corresponding to the foreground object, and, based on the identified object class, determining a target value for an object property of the foreground object. The example method may also include applying a transformation function to transform the foreground object into a transformed object, where the transformation function modifies the object property of the foreground object from having an initial value to having the target value. The example method may further include transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.

Abstract: Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

Abstract: Systems and methods for augmenting a data set are provided. An example method may include locating a foreground object disposed within a seed image, identifying an object class corresponding to the foreground object, and, based on the identified object class, determining a target value for an object property of the foreground object. The example method may also include applying a transformation function to transform the foreground object into a transformed object, where the transformation function modifies the object property of the foreground object from having an initial value to having the target value. The example method may further include transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.

Abstract: Systems and methods for augmenting a data set are provided. An example method may include locating a foreground object disposed within a seed image, identifying an object class corresponding to the foreground object, and, based on the identified object class, determining a target value for an object property of the foreground object. The example method may also include applying a transformation function to transform the foreground object into a transformed object, where the transformation function modifies the object property of the foreground object from having an initial value to having the target value. The example method may further include transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to generate, based at least in part on the text, an image based search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the image based search query.

Abstract: Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

Abstract: Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

Abstract: Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to identify, based at least in part on the text, a searchable entity or trigger phrase, generate, based on the searchable entity or trigger phrase, a search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the search query.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output for display a graphical keyboard comprising a plurality of keys, determine, based on an indication of a selection of one or more keys from the plurality of keys, inputted, determine, based on the inputted text, an information category associated with the inputted text, determine, based on the information category, a graphical symbol associated with the information category, and output, for display, the graphical symbol in a suggestion region of the graphical keyboard.

Abstract: A computing device is described that outputs, for display, a graphical keyboard comprising a plurality of keys, determines, based at least in part on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication, and determines, based at least in part on the text, an implied user-expression that characterizes at least a portion of the text. The computing device generates a phrase of one or more iconographic symbols that represent the implied user-expression, and outputs, for display within the graphical keyboard, a graphical indication of the phrase.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to generate, based at least in part on the text, an image based search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the image based search query.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to identify, based at least in part on the text, a searchable entity or trigger phrase, generate, based on the searchable entity or trigger phrase, a search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the search query.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output for display a graphical keyboard comprising a plurality of keys, determine, based on an indication of a selection of one or more keys from the plurality of keys, inputted, determine, based on the inputted text, an information category associated with the inputted text, determine, based on the information category, a graphical symbol associated with the information category, and output, for display, the graphical symbol in a suggestion region of the graphical keyboard.

Abstract: A computing device is described that includes at least one processor and a memory including instructions that when executed cause the at least one processor to output, for display, a graphical keyboard comprising a plurality of keys, and determine, based on an indication of a selection of one or more keys from the plurality of keys, text of an electronic communication. The instructions, when executed, further cause the at least one processor to identify, based at least in part on the text, a searchable entity or trigger phrase, generate, based on the searchable entity or trigger phrase, a search query, and output, for display, within the graphical keyboard, a graphical indication to indicate that the computing device generated the search query.