A user device provides a user interface for video manipulation with face replacement. A method of implementations includes accessing a video comprising a plurality of frames that comprise one or more faces, providing a plurality of stickers comprising alternate face graphics for the one or more faces, receiving, via a user interface of a user device, user selection of one of the stickers and a selected face of the one or more faces, accessing a plurality of face frame sequences of the video, wherein each face frame sequence is a sequence of frames of the video comprising the selected face of the one or more faces, and replacing the selected face with the selected sticker in a first face frame sequence of the plurality of face frame sequences and in a second face frame sequence of the plurality of face frame sequences.

A method for driver identification including recording a first image of a vehicle driver; extracting a set of values for a set of facial features of the vehicle driver from the first image; determining a filtering parameter; selecting a cluster of driver identifiers from a set of clusters, based on the filtering parameter; computing a probability that the set of values is associated with each driver identifier of the cluster; determining, at the vehicle sensor system, driving characterization data for the driving session; and in response to the computed probability exceeding a first threshold probability: determining that the new set of values corresponds to one driver identifier within the selected cluster, and associating the driving characterization data with the one driver identifier.

A system, method and computer program product for use in managing collaboration on documents having digital ink on a network of computing devices is disclosed. Each computing device has a processor and at least one system application for processing handwriting input under control of the processor. The system application displays, on a display associated with one of the computing devices, a document having digital ink based on a journal of the document, defines the journal to have journal entries associated with at least handwriting input to the document represented by the digital ink, and communicates the journal entries of the journal with one or more of the other networked computing devices displaying the document. The handwriting input associated with the journal entries is handwriting input to the document via the input interface of any of the computing devices displaying the document based on the communicated journal entries.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

An apparatus for recognizing an object in an image includes a preprocessing module configured to receive an image including an object and to output a preprocessed image by performing image enhancement processing on the received image to improve a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.

Provided are an illegal building identification method and apparatus, a device, and a storage medium, which relate to the field of cloud computing. The specific implementation scheme is: acquiring a target image and a reference image associated with the target image; extracting a target building feature of the target image and a reference building feature of the reference image, respectively; and determining, according to the target building feature and the reference building feature, an illegal building identification result of the target image.

An object of the present disclosure is to provide an information processing apparatus, an information processing system, an information processing method, and an information processing program capable of achieving efficient use of training data. An information processing apparatus according to the present disclosure includes: a recognition unit (101) that performs object recognition processing using sensor information acquired by a sensor, the object recognition processing being performed by a first recognizer that has been pretrained; and a training data application determination unit (22d) that determines whether the sensor information is applicable as training data to a second recognizer different from the first recognizer.

An image sensor is used to capture an image that includes a plurality of objects. Presence and location data is identified for the plurality of objects. The image and the presence and location data is utilized to create individual representations of the plurality of objects. The plurality of objects are classified through employment of the individual representations. A machine learning model is updated with the classification data generated by classifying the plurality of objects.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for swapping one or more faces without any explicit training on the one or more faces. The proposed method can be further implemented in real time.

A method the following operations: downscaling an input image to generate a scaled image; performing, to the scaled image, a first convolutional neural networks (CNN) modeling process with first non-local operations, to generate global parameters; and performing, to the input image, a second CNN modeling process with second non-local operations that are performed with the global parameters, to generate an output image corresponding to the input image. A system is also disclosed herein.