Abstract: Systems and methods for providing visual indications of generative model responses can include obtaining a user input and processing the user input with a generative model to generate a model-generated-response. The systems and methods can process the model-generated response and an image of an environment to generate an augmented image. The augmented image can include visual indicators of the model-generated response, which can include annotating the image based on detected features within the image. Generation of the augmented image can include object detection and annotation based on the content of the model-generated response.

Abstract: A multimodal search system is described. The system can receive image data captured by a camera of a user device. Additionally, the system can receive audio data associated with the image data. The audio data can be captured by a microphone of the user device. Moreover, the system can process the image data to generate visual features. Furthermore, the system can process the audio data to generate a plurality of words. The system can generate a plurality of search terms based on the plurality of words and the visual features. Subsequently, the system can determine one or more search results associated with the plurality of search terms and provide the one or more search results as an output.

Abstract: Systems and methods for providing scene understanding can include obtaining a plurality of images, stitching images associated with the scene, detecting objects in the scene, and providing information associated with the objects in the scene. The systems and methods can include determining filter tags or query tags that can be selected to filter the plurality of objects, which can then be provided as information to the user to provide further insight on the scene. The information may be provided in an augmented-reality experience via text or other user-interface elements anchored to objects in the images.

Abstract: Systems and methods for textual replacement can include the determination of a visual intent, which can trigger an interface for selecting an image to replace visual descriptors. The visually descriptive terms can be identified, and an indicator can be provided to indicate the text replacement option may be initiated. An image can then be selected by a user to replace the visually descriptive terms.

Abstract: A multimodal search system is described. The system can receive image data from a user device. Additionally, the system can receive a prompt associated with the image data. Moreover, the system can determine, using a computer vision model, a first object in the image data that is associated with the prompt. Furthermore, the system can receive, from the user device, a user indication on whether the image data includes the first object. Subsequently, in response to receiving the user indication, the system can generate a response using a large language model.

Abstract: Systems and methods for multi-image search can include obtaining two or more images and determining one or more search results that are based on the two or more images. The one or more search results can be determined based on determined shared attributes of the two or more images. The one or more search results may be based on feature embeddings associated with the two or more images. The two or more images may be obtained based on one or more user interactions with one or more databases.

Abstract: Systems and methods for providing visual indications of generative model responses can include obtaining a user input and processing the user input with a generative model to generate a model-generated-response. The systems and methods can process the model-generated response and an image of an environment to generate an augmented image. The augmented image can include visual indicators of the model-generated response, which can include annotating the image based on detected features within the image. Generation of the augmented image can include object detection and annotation based on the content of the model-generated response.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for responding to requests associated with an image. A computing system obtains, wherein the image depicts a first set of textual content. The computing system determines one or more characteristics of the first set of textual content. The computing system determines a response type from a plurality of response types based on the one or more characteristics. The computing system generates a model input, wherein the model input comprises data descriptive of the first set of textual content and a prompt associated with the response type. The computing system provides providing the model input as an input to a machine-learned language model. The computing system receives a second set of text as an output of the machine-learned language model as a result of the machine-learned language model processing the model input.

Abstract: Systems and methods for providing scene understanding can include obtaining a plurality of images, stitching images associated with the scene, detecting objects in the scene, and providing information associated with the objects in the scene. The systems and methods can include determining filter tags or query tags that can be selected to filter the plurality of objects, which can then be provided as information to the user to provide further insight on the scene. The information may be provided in an augmented-reality experience via text or other user-interface elements anchored to objects in the images.

Abstract: Systems and methods for textual replacement can include the determination of a visual intent, which can trigger an interface for selecting an image to replace visual descriptors. The visually descriptive terms can be identified, and an indicator can be provided to indicate the text replacement option may be initiated. An image can then be selected by a user to replace the visually descriptive terms.

Abstract: A multimodal search system using a video query is described. The system can receive video data captured by a camera of a user device. The video data can have a sequence of image frames. Additionally, the system can receive audio data associated with the video data captured by the user device. Moreover, the system can process, using one or more machine-learned models, the sequence of image frames to generate video embeddings related to the sequence of the image frames. The video embeddings can have a plurality of image embeddings associated with the sequence of image frames. Furthermore, the system can determine one or more video results based on the video embeddings and the audio data. Subsequently, the system can transmit, to the user device, the one or more video results.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Abstract: Result images are retrieved based on a similarity to a query image. A set of textual inputs is processed with a machine-learned language model to obtain a language output comprising textual content, wherein the set of textual inputs comprises textual content from source documents that include the result images, and a prompt associated with the query image. The language output and the result images are provided to a user computing device. Information is received descriptive of an indication by a user that a first result image is visually dissimilar to the query image. Textual content associated with the source document that includes the first result image from the set of textual inputs is removed. The set of textual inputs is processed with the machine-learned language model to obtain a refined language output. The refined language output is provided to the user computing device.

Abstract: A result image is retrieved based on a similarity between a query image and the result image. A first unit of text is obtained, wherein the first unit of text comprises at least a portion of textual content of a source document that includes the result image. A second unit of text is determined responsive to a prompt associated with the query image, wherein the second unit of text comprises one or more of (a) at least some of the first unit of text, or (b) text derived from the first unit of text. The second unit of text and the result image are provided for display within an interface.

Abstract: A multimodal search system is described. The system can receive image data captured by a camera of a user device. Additionally, the system can receive audio data associated with the image data. The audio data can be captured by a microphone of the user device. Moreover, the system can process the image data to generate visual features. Furthermore, the system can process the audio data to generate a plurality of words. The system can generate a plurality of search terms based on the plurality of words and the visual features. Subsequently, the system can determine one or more search results associated with the plurality of search terms and provide the one or more search results as an output.

Abstract: Systems and methods for multi-image search can include obtaining two or more images and determining one or more search results that are based on the two or more images. The one or more search results can be determined based on determined shared attributes of the two or more images. The one or more search results may be based on feature embeddings associated with the two or more images. The two or more images may be obtained based on one or more user interactions with one or more databases.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for responding to requests associated with an image. A computing system obtains, wherein the image depicts a first set of textual content. The computing system determines one or more characteristics of the first set of textual content. The computing system determines a response type from a plurality of response types based on the one or more characteristics. The computing system generates a model input, wherein the model input comprises data descriptive of the first set of textual content and a prompt associated with the response type. The computing system provides providing the model input as an input to a machine-learned language model. The computing system receives a second set of text as an output of the machine-learned language model as a result of the machine-learned language model processing the model input.

Abstract: Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.

Abstract: Systems and methods for textual replacement can include the determination of a visual intent, which can trigger an interface for selecting an image to replace visual descriptors. The visually descriptive terms can be identified, and an indicator can be provided to indicate the text replacement option may be initiated. An image can then be selected by a user to replace the visually descriptive terms.