Abstract: In one embodiment, a method includes accessing a first set of images of multiple images of a scene, wherein the first set of images show the scene during a time period. The method includes generating, by processing the first set of images using a first machine-learning model, one or more attributes representing observed actions performed in the scene during the time period. The method includes predicting, by processing the generated one or more attributes using a second machine-learning model, one or more actions that would happen in the scene after the time period.

Abstract: In one embodiment, a method includes accessing a stream of F video frames, where each of the F video frames includes N patches that are non-overlapping, generating an initial embedding vector for each of the N×F patches in the F video frames, generating a classification embedding by processing the generated N×F initial embedding vectors using a self-attention-based machine-learning model that computes a temporal attention and a spatial attention for each of the N×F patches, and determining a class of the stream of video frames based on the generated classification embedding.

Abstract: In one embodiment, a method includes accessing first sets of tokens associated with a desired task and one or more modalities associated with a context of the desired task, determining a second set of tokens for each of the one or more modalities using a classifier network associated with the modality, generating a number of embedding vectors by mapping the first sets of tokens and the second set of tokens associated with each of the one or more modalities to an embedding space, and producing a sequence of words addressing the desired task by processing the number of embedding vectors with an encoder-decoder network.

Abstract: In one embodiment, a method includes receiving a request for information associated with a video, determining the information associated with the video by processing the video using a machine-learning model which is based on a convolutional neural network comprising a plurality of layers, wherein at least one of the plurality of layers comprises one or more building blocks, wherein at least one of the one or more building blocks comprises a first filter configured to perform a three-dimensional (3D) pointwise convolutional operation and a second filter configured to perform a three-dimensional (3D) groupwise convolutional operation, and outputting the information associated with the video in response to the request.

Abstract: In one embodiment, a method includes accessing a first set of images of multiple images of a scene, wherein the first set of images show the scene during a time period. The method includes generating, by processing the first set of images using a first machine-learning model, one or more attributes representing observed actions performed in the scene during the time period. The method includes predicting, by processing the generated one or more attributes using a second machine-learning model, one or more actions that would happen in the scene after the time period.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: A system includes an image analysis module that is configured to programmatically analyze individual images in a collection of images in order to determine information about each image in the collection. The system may also include a manual interface that is configured to (i) interface with one or more human editors, and (ii) displays a plurality of panels concurrently. Individual panels may be provided for one or more analyzed images, and individual panels may be configured to display information that is at least indicative of the one or more images of that panel and/or of the information determined from the one or more images.

Abstract: Embodiments enable searching of portions of objects in images, including programmatically analyzing each image in a collection in order to determine image data that, for individual images in the collection, represents one or more visual characteristics of a portion of an object shown in that image. A user is enabled to specify one or more search criteria that includes image data, and a search result may be determined based on one or more images in the collection that show a corresponding object that has a portion that satisfies a threshold. The threshold is defined at least in part by the one or more search criteria.

Abstract: Embodiments described herein provide a system and method for providing merchandise items at a network site. According to an embodiment, an image of a merchandise item is obtained. The image is programmatically analyzed to determine information about the merchandise item. The information is used to generate a presentation that includes the merchandise item.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: A system includes an image analysis module that is configured to programmatically analyze individual images in a collection of images in order to determine information about each image in the collection. The system may also include a manual interface that is configured to (i) interface with one or more human editors, and (ii) displays a plurality of panels concurrently. Individual panels may be provided for one or more analyzed images, and individual panels may be configured to display information that is at least indicative of the one or more images of that panel and/or of the information determined from the one or more images.

Abstract: Embodiments enable searching of portions of objects in images, including programmatically analyzing each image in a collection in order to determine image data that, for individual images in the collection, represents one or more visual characteristics of a portion of an object shown in that image. A user is enabled to specify one or more search criteria that includes image data, and a search result may be determined based on one or more images in the collection that show a corresponding object that has a portion that satisfies a threshold. The threshold is defined at least in part by the one or more search criteria.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: Content-based information retrieval is described. In an example, a query item such as an image, document, email or other item is presented and items with similar content are retrieved from a database of items. In an example, each time a query is presented, a classifier is formed based on that query and using a training set of items. For example, the classifier is formed in real-time and is formed in such a way that a limit on the proportion of the items in the database that will be retrieved is set. In an embodiment, the query item is analyzed to identify tokens in that item and subsets of those tokens are selected to form the classifier. For example, the subsets of tokens are combined using Boolean operators in a manner which is efficient for searching on particular types of database.

Abstract: Embodiments enable searching of portions of objects in images, including programmatically analyzing each image in a collection in order to determine image data that, for individual images in the collection, represents one or more visual characteristics of a portion of an object shown in that image. A user is enabled to specify one or more search criteria that includes image data, and a search result may be determined based on one or more images in the collection that show a corresponding object that has a portion that satisfies a threshold. The threshold is defined at least in part by the one or more search criteria.

Abstract: Embodiments described herein provide a system and method for providing merchandise items at a network site. According to an embodiment, an image of a merchandise item is obtained. The image is programmatically analyzed to determine information about the merchandise item. The information is used to generate a presentation that includes the merchandise item.

Abstract: Embodiments described herein provide for a system for creating a data collection of recognized images. The system includes an image analysis module that is configured to programmatically analyze individual images in a collection of images in order to determine information about each image in the collection. The system may also include a manual interface that is configured to (i) interface with one or more human editors, and (ii) displays a plurality of panels concurrently. Individual panels may be provided for one or more analyzed images, and individual panels may be configured to display information that is at least indicative of the one or more images of that panel and/or of the information determined from the one or more images. Additionally, the manual interface enables the one or more human editors to view the plurality of panels concurrently and to interact with each of the plurality of panels in order to correct or remove any information that is incorrectly determined from the image of that panel.