Xiaomi Inc.

Siemens Aktiengesellschaft

Baidu Online Network Technology (Beijing) Co., Ltd.

Fei Long

Zhijun Chen

Tao N Zhang

Wendi Hou

Pingze Wang

Haifeng Wang

Shun Zhang

Xiaogang Wang

Qiuping Qin

Ming Jie

Li Pan

Tao Zhang

Shiyu Li

Quan Wen

Wenwen Liu

Yan Hong

Daoping Sun

Yayong Guan

Detlef Pauly

The present disclosure provides an elevator control method, apparatus, electronic device, storage medium and system, which relate to artificial intelligence technology. The method includes: obtaining a user instruction, where the user instruction is determined by a speech device based on a user's speech content; recognizing the user instruction and converting the user instruction into an elevator control instruction; and controlling an elevator via the elevator control instruction. The elevator control method provided by the present disclosure enables the user to control the elevator through the speech device without touching the elevator, and thereby reducing potential safety risk caused by multiple people touching the elevator.

Elevator control method, apparatus, electronic device, storage medium and system

A method, device and medium for constructing a knowledge base is described, wherein the knowledge base construction based on retrieving text information, determining whether the text information includes at least first information according to a predefined property description pattern, extracting the first information from the text information when the first information is determined to be included in the text information, and constructing the knowledge base based on the first information and an entity corresponding to the first information.

Method, device and medium for knowledge base construction

A photo sharing method includes: receiving, from a user, a party photo sharing instruction regarding a to-be-shared party photo set, where the to-be-shared party photo set has party marker information and includes at least one party photo; acquiring a face recognition result by conducting face recognition on each party photo contained in the to-be-shared party photo set, and determining a face image set contained in the to-be-shared party photo set according to the face recognition result; determining, from each contact photo contained in a contact list, each target contact photo which matches with each face image in the face image set respectively; and sending the to-be-shared party photo set to each target contact respectively according to contact information corresponding to the each target contact photo.

Photo sharing method and device

A method for identifying a feature of an image is provided. The method includes: segmenting an image into a plurality of blocks, wherein each block includes a plurality of cells; transforming pixels of each cell from a spatial domain to a frequency domain; and identifying a Histogram of Oriented Gradient (HOG) feature of the image in the frequency domain.

Method, device and computer-readable medium for identifying feature of image

A method for determining a spam caller phone number is disclosed. The method may include obtaining N suspicious numbers in a call record set, wherein the N suspicious numbers are first N unknown numbers in a predetermined number of target call records that have the highest frequencies of appearance, determining whether a spam caller feature word recorded in a preset dictionary exists in keywords contained in a target call record of each suspicious number, and if a spam caller feature word recorded in the preset dictionary exists in keywords contained in the target call record of a suspicious number, determining the suspicious number to be a spam caller phone number.

Method, device, and system for determining spam caller phone number

The present disclosure relates to an image classification and clustering method and device. Two images containing human faces may be classified into one cluster or different clusters based on a cluster merging threshold adaptively determined according to the combinational face poses in the two images. The adaptive cluster merging threshold help reduce false positive and false negative classification of human face containing images into clusters.

Image classification method and device

A method for characteristic extraction includes dividing an image into a plurality of blocks, each block including a plurality of cells. The method also includes performing sparse signal decomposition for each cell to obtain a sparse vector corresponding to each cell. The method further includes extracting characteristics of Histogram of Oriented Gradient (HOG) of the image according to the sparse vectors.

Method, device, and storage medium for image characteristic extraction

Method, device and computer-readable medium for sensitive picture recognition are provided in the disclosure. Aspects of the disclosure provide a method for sensitive picture recognition. The method includes receiving a picture to be processed from a picture library associated with a user account, applying a sensitive picture recognition model to the picture to determine whether the picture is a sensitive picture or not, and providing a privacy protection associated with the user account to the picture when the picture is the sensitive picture. In an example, the method includes storing the picture in a private album under the user account with access security protection.

Method, device and computer-readable medium for sensitive picture recognition

A method and a device for area identification are provided in the disclosure. The method includes: binarizing a text area including a row of characters; calculating a histogram in a vertical direction of the binarized text area, wherein the histogram includes abscissas of pixels in each column and corresponding accumulated values of foreground color pixels of the pixels in each column; and identifying a character area of each of one or more characters in the text area according to distribution information of the accumulated values.

Method and device for character area identification

A method for area extraction includes obtaining an area position of a first information area in a document image, determining a second information area according to the area position of the first information area, and segmenting the second information area to obtain at least one character zone.

Method, device, and computer-readable storage medium for area extraction

A video may be categorized into a picture category or a video category. A key frame of the video includes a face and a face feature in the key frame is obtained. Face features respectively associated with a plurality of picture categories are acquired and the video is assigned to one of the picture categories based on a comparison of the key frame face feature and the face features of the picture categories. Videos may first be associated with a video category by comparing key frame face features from the videos, and then the video category may be assigned to a picture category based on comparison of a video category face feature with a plurality of picture category face features. Alternatively, a video may be assigned to a picture category based on matching capture times and capture locations between the video and a reference picture in the picture category.

Video categorization method and apparatus, and storage medium

A method for area identification includes identifying a plurality of candidate predefined edges in a document image of a document. The candidate predefined edges are edges in a predefined direction of the document. The method further includes determining one of the candidate predefined edges to be a target predefined edge and identifying at least one information area in the document image based on the target predefined edge.

Method, apparatus, and computer-readable storage medium for area identification

A method and device for determining semantic similarity between two character strings are disclosed. The two character strings are segmented into sequences of words or phrases which represent the correlation between the characters. Edit distance from the first sequence to the second sequence is calculated based on a predetermined algorithm. A minimum semantic distance is then determined from the edit distance by considering the word/phrase pairs appearing in both sequences and the relationship between the cost of the various operations performed to convert the first sequence into the second sequence. The semantic similarity between the two character strings is then determined and normalized from the minimum semantic distance.

Method and apparatus for determining semantic similarity of character strings

The present disclosure relates to a method and a device for intelligently guiding a user to ride an elevator/escalator in a building complex such as a shopping mall. Based on a current location and floor level of a user in the building complex and a target floor and location, and a 3D model of the building complex, either an elevator path or escalator path may be automatically and intelligently selected from possible paths.

Method and device for intelligently guiding a user to ride elevator/escalator

A method for displaying images includes clustering similar images in an album into a plurality of image groups when receiving an instruction for opening the album, selecting representative images from the image groups, and displaying the representative images.

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Beijing, China

Fei Long