A non-transitory computer-readable medium is provided. The medium stores a program executable by a processor of a client to receive an image file from a server and present the image file to a user of the client. In response to an operation performed by the user on the image file, a media information presentation request is sent to the server. A notification message of to-be-presented media information is received from the server. The notification message includes brief information of the to-be-presented media information. The brief information is presented to the user of the client. A media file presentation request is sent to the server in response to an operation performed by the user on the brief information. A link address of a media file is received from the server. The media file according to the link address is obtained and presented to the user of the client.

A method for transporting goods on a pallet, where the pallet may be reversibly converted to a bin for the collection of waste and/or recyclable material. The pallet may include a base with side walls and a collapsible sleeve wherein, if the collapsible sleeve is open and joined to the base, the collapsible sleeve, with the base, forms a bin. Goods are transported on the pallet. After unloading the pallets and removing the goods, the pallet may be converted to a bin. Waste or recyclable material may be placed in the bin and transported away. After removing the material in the bin and cleaning the bin, the bin may be collapsed back into a pallet for the transportation of goods.

A method for transporting goods on a pallet, where the pallet may be reversibly converted to a bin for the collection of waste and/or recyclable material. The pallet may include a base with side walls and a collapsible sleeve wherein, if the collapsible sleeve is open and joined to the base, the collapsible sleeve, with the base, forms a bin. Goods are transported on the pallet. After unloading the pallets and removing the goods, the pallet may be converted to a bin. Waste or recyclable material may be placed in the bin and transported away. After removing the material in the bin and cleaning the bin, the bin may be collapsed back into a pallet for the transportation of goods.

Provided are methods, systems, and devices for generating annotations in images that can include receiving image data including images associated with locations. The images can include key images comprising one or more key annotations located at one or more key annotation locations in the one or more key images. At least one image and a pair of the key images that satisfies one or more annotation criteria can be selected based in part on one or more spatial relationships of the plurality of locations associated with the images. An annotation location for an annotation in the image can be determined based in part on the one or more key annotation locations of the one or more key annotations in the pair of the key images that satisfies the one or more annotation criteria. An annotation can be generated at the annotation location of the image.

An electronic computing device includes a processing unit and system memory. The system memory includes instructions which, when executed by the processing unit, cause the electronic computing device to receive data associated with one or more customers of an institution. The data is received from one or more other electronic computing devices. The received data is analyzed to identify grammatical elements in the data. Relationships are derived between a plurality of the grammatical elements. At least one derived relationship is used to update a profile for a customer. At least one derived relationship is used to identify a customer for which a remedial action is warranted.

The present disclosure is related to visual annotations on medical images. A system may include a processor configured to process input data and identify a relationship amongst received input data in a data set. The system may also include an aggregator coupled to the processor and configured to receive processed data from the processor and aggregate data within the data set while maintaining one or more data relationships within the data set. Further, the system may include an annotation service module coupled to the aggregator and configured to generate at least one annotation that is maintained across at least a portion of the data within the data set.

Techniques for separating shareable images from non-shareable images. In various implementations, image metadata and feature analysis may be used to evaluate the shareability of a photograph associated with a particular user. In some implementations, single photos may be determined to be shareable. In another implementation, an event associated with multiple photos may be determined to be shareable. In some implementations, a photo may be determined to be shareable with a single recipient. In another implementation, a photo may be determined to be shareable with multiple recipients. In yet another implementation, these techniques may be assisted by supervised machine learning. In still yet another implementation, photos determined to be shareable may be suggested to a user for sharing, or automatically shared, per an opt-in feature.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using location data to identify and provide services in association with items appearing in captured images. One of the methods includes receiving, from a device, an image and location data representing the device's physical location, determining, based on the location data, that a particular set of one or more locations are within a threshold distance of the device's physical location, accessing, for each of the one or more locations in the particular set, item information that indicates one or more items that are associated with the location, determining, based on the accessed item information, that the image likely shows a particular item that is associated with one or more locations in the particular set, and providing, to the device, instructions for presentation of information about (i) the particular item and (ii) one or more locations in the particular set that are associated with the particular item.

A learning method for transforming a virtual video on a virtual world to a more real-looking video is provided. And the method includes steps of: (a) a learning device instructing a generating CNN to apply a convolutional operation to an N-th virtual training image, N-th meta data and (N-K)-th reference information to generate an N-th feature map; (b) the learning device instructing the generating CNN to apply a deconvolutional operation to the N-th feature map to generate an N-th transformed image; (c) the learning device instructing a discriminating CNN to apply a discriminating CNN operation to the N-th transformed image to generate a category score vector; (d) the learning device instructing the generating CNN to generate a generating CNN loss by referring to the category score vector and its corresponding GT, and to perform backpropagation by referring to the generating CNN loss to learn parameters of the generating CNN.

A learning method for transforming a virtual video on a virtual world to a more real-looking video is provided. And the method includes steps of: (a) a learning device instructing a generating CNN to apply a convolutional operation to an N-th virtual training image, N-th meta data and (N-K)-th reference information to generate an N-th feature map; (b) the learning device instructing the generating CNN to apply a deconvolutional operation to the N-th feature map to generate an N-th transformed image; (c) the learning device instructing a discriminating CNN to apply a discriminating CNN operation to the N-th transformed image to generate a category score vector; (d) the learning device instructing the generating CNN to generate a generating CNN loss by referring to the category score vector and its corresponding GT, and to perform backpropagation by referring to the generating CNN loss to learn parameters of the generating CNN.