A computing system receives, from a client device, image data describing an image captured by an optical sensor of the client device. The computing system compares the image to a set of reference images that include associated metadata describing a real-world feature depicted by the respective reference image. The computing system determines, based on the comparison, a subset of reference images that are similar to the image, and then determines, based on associated metadata of the subset of reference images, that the image captured by the optical sensor of the client device depicts a first real-world feature. The computing system selects a subset of media overlays related to the first real-world feature based on metadata associated with each media overlay that describes the respective media overlay. The computing system transmits the subset of media overlays to the client device.

An image retrieval system may receive an image query that includes image data. The image retrieval system may determine an image descriptor based on the image data. The image retrieval system may obtain a text descriptor associated with the image descriptor in the descriptor repository. The image retrieval system may generate a document query comprising a search parameter, the search parameter including the text descriptor. The image retrieval system may identify, in a document database, text documents based on the document query. The text documents may be associated with document identifiers. The image retrieval system may obtain, from the file mapping repository, image identifiers associated with the document identifiers. The image query result from the image retrieval system may reference images associated with the image identifiers.

A system is provided for identifying damages of a vehicle. During operation, the system can obtain a set of digital images associated with a set of tagged digital images as training data. Each tagged digital image in the set of tagged digital images may include at least one damage object. The system can train a damage identification model based on the training data. When training the damage identification model, the system may identify at least a damage object in the training data based on a target detection technique. The system may also generate a set of feature vectors for the training data. The system can use the set of feature vectors to optimize a set of parameters associated with the damage identification model to obtain a trained damage identification model. The system can then apply the trained damage identification model to obtain a damage category prediction result.

An online system may identify content with which a user has an interest. For example, the online system may determine that a user has an interest in the content based on interaction information indicating that the user interacted with the content. In a particular example, the online system may identify image concepts included in the content based on computer vision techniques that recognize the image concepts. The online system may model probabilities that image concepts will appeal to users. Based on the modeled probabilities, the online system may automatically recommend image concepts for inclusion in candidate images, automatically generate candidate images, or assess candidate images to determine a probability of user interaction with the assessed candidate images.

In some aspects, the described systems and methods provide for a method for predicting tissue characteristics for a pathology image. A statistical model trained on multiple annotated pathology images is used. Each of the training pathology images includes an annotation describing tissue characteristics for one or more portions of the image. The method includes accessing a pathology image for predicting tissue characteristics. A trained statistical model is retrieved from a storage device. A set of patches is defined from the pathology image. Each of the patches in the set includes a subset of pixels from the corresponding pathology image. The set of patches is processed using the trained statistical model to predict respective annotations for each patch in the set. The predicted annotations are stored on the storage device.

Methods, systems and computer program products for updating a social media post based on subsequent related social media content. Aspects include receiving first social media content and second social media content, wherein the first and second social media are displayed in a respective first and second social media posts associated with a user account. Aspects also include determining that the first social media content is correlated to the second social media content based on contextual analysis. Aspects also include updating the first social media post to include an indication of the second social media post.

Methods, systems and computer program products for updating a social media post based on subsequent related social media content. Aspects include receiving first social media content and second social media content, wherein the first and second social media are displayed in a respective first and second social media posts associated with a user account. Aspects also include determining that the first social media content is correlated to the second social media content based on contextual analysis. Aspects also include updating the first social media post to include an indication of the second social media post.

A management system includes: an information storage member that stores individual identification information of a management target; an information reading device that reads individual identification information I from the information storage member located within a predetermined distance from the information reading device; an imaging device hat generates continuous image data by continuously capturing images of at least an area where the information reading device can read the individual identification information I from the information storage member; a storage device that stores the continuous image data; a control device that acquires an event occurrence time at which an event related to the individual identification information I read by the information reading device has occurred, and sets in the continuous image data a playback start time corresponding to the event occurrence time; and a display device capable of displaying an image based on the continuous image data.

For searching virtual content information, a processor searches the virtual content information including a virtual object contour. The processor further retrieves an image based on the virtual content information.

A method, computer system, and computer program product for accelerating class data loading in a containers environment are provided. In response to a first container in a containers environment being created from a first image, at least one archive file containing a set of classes from the first image can be loaded. Then a respective class sharing file for each of the at least one archive file can be generated. The class sharing file is stored in a shared location. A second container in the containers environment is created from a second image. If a class sharing file from the archive is found in the shared location, that class sharing file can be used.