Huawei Technologies Co., Limited

Songcen Xu

Youliang Yan

Jianzhuang Liu

Xiaofei Wu

Li Qian

Jun Yue

Bin Shao

Xueyan Huang

Juwei Lu

Wei Zhou

Fei Huang

Peng Dai

Zhihao Li

Sayem Mohammad Siam

Chunhua Shen

Yiren Zhou

Yi Liu

Liang Wang

Hao Chen

Min Wang

Yu Wang

Jun He

Fenglong Song

Weicai Zhong

Liangwei Wang

Yuzheng Zhuang

Chuanjian Liu

Daxin Luo

Rongfu Zheng

Xueyi Zou

Lei Hao

Yajiao Liu

Kunyang Sun

Tong He

Zhi Tian

Zhenhua Zhao

A method and apparatus for updating an object recognition model in the field of artificial intelligence are disclosed. According to the method, a target image and first voice information of a user are obtained. The first voice information indicates a first category of a target object in the target image. A feature library of a first object recognition model is updated based on the target image and the first voice information. The updated first object recognition model includes a feature of the target object and a first label indicating the first category, and the feature of the target object corresponds to the first label. A recognition rate of an object recognition model can be improved more easily according to the technical solution provided in this application.

Method and apparatus for updating object recognition model

This application discloses an object recognition method and apparatus in the field of artificial intelligence. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: obtaining one or more body regions of a to-be-recognized image; determining a saliency score of each of the one or more body regions; and when a saliency score of a body region A is greater than or equal to a categorization threshold, determining a feature vector of an object in the body region A based on a feature of the object in the body region A, and determining a category of the object in the body region A based on the feature vector of the object in the body region A and a category feature vector in a feature library, where the body region A is any one of the one or more body regions.

Object recognition method and apparatus

In one example method, a first image including M objects is obtained. For N objects in the M objects, when N is greater than or equal to 2, arrangement orders of the N objects is determined, where an arrangement order of any one of the N objects is determined based on at least one of a scene intent weight, a confidence score, or an object relationship score. The scene intent weight is used to indicate a probability that the any object is searched in a scene corresponding to the first image, the confidence score is a similarity between the any object and an image in an image library, and the object relationship score is used to indicate importance of the any object in the first image. Search results of some or all of the N objects are fed back according to the arrangement orders of the N objects.

Search result feedback method and apparatus, and storage medium

An instance segmentation method and apparatus are provided. A to-be-trained segmentation network performs the following processing on each instance group that is in a sample original image and that is of pixels of a marked instance, where each instance group includes at least one marked instance, and the processing includes: predicting at least two different first basic feature maps and a first attention feature map corresponding to each first basic feature map; performing weighted processing on the at least two first basic feature maps and pixel values of respective first attention feature maps corresponding to the at least two first basic feature maps, to obtain a first feature fusion map; and training the to-be-trained segmentation network based on the first feature fusion map and the sample original image. A segmentation model can precisely determine pixels of an instance in an original image.

Instance segmentation method and apparatus

A video generation method may be applied to the field of image processing and video generation in the field of artificial intelligence. The method includes: receiving a video generation instruction, and obtaining text information and image information in response to the video generation instruction, where the text information includes one or more keywords, and the image information includes N images; obtaining, based on the one or more keywords, one or more image features that is in each of the N images and corresponds to the one or more keywords; and inputting the one or more keywords and the one or more image features of the N images into a target generator network to generate a target video, where the target video includes M images, and the M images are generated based on the one or more image features of the N images and correspond to the one or more keywords.

Video generation method and related apparatus

The disclosure provides gesture recognition methods and apparatuses, and relate to the field of artificial intelligence. One example gesture recognition method includes obtaining an image stream, and determining, based on a plurality of consecutive frames of hand images in the image stream, whether a user makes a preparatory action. When the user makes the preparatory action, continuing to obtain an image stream, and determining a gesture action of the user based on a plurality of consecutive frames of hand images in the continuously obtained image stream. Next, further responding to the gesture action to implement gesture interaction with the user. In this application, in order to reduce the erroneous recognition occurring in a gesture recognition process, the preparatory action is determined before gesture recognition is performed.

Gesture recognition method, electronic device, computer-readable storage medium, and chip

Methods and systems for gesture-based control of a device are described. An input frame is processed to determine a location of a distinguishing anatomical feature in the input frame. A virtual gesture-space is defined based on the location of the distinguishing anatomical feature, the virtual gesture-space being a defined space for detecting a gesture input. The input frame is processed in only the virtual gesture-space, to detect and track a hand. Using information generated from detecting and tracking the at least one hand, a gesture class is determined for the at least one hand. The device may be a smart television, a smart phone, a tablet, etc.

Methods and systems for hand gesture-based control of a device

A vehicle control apparatus and method includes, when a self-driving vehicle is in a self-driving status, obtaining driving status information of the self-driving vehicle and first driving environment information of the self-driving vehicle; calculating a coarse-grained control instruction of the self-driving vehicle based on the driving status information, the first driving environment information, and a control instruction decision model; determining, based on second driving environment information, whether the coarse-grained control instruction is to be executed; and when the coarse-grained control instruction is to be executed, determining, based on lane information and the driving status information of the self-driving vehicle, a fine-grained control instruction corresponding to the coarse-grained control instruction.

Vehicle control method, apparatus, and device

A method for optimizing a photographing pose of a user, where the method is applied to an electronic device, and the method includes: displaying a photographing interface of a camera of the electronic device; obtaining a to-be-taken image in the photographing interface; determining, based on the to-be-taken image, that the photographing interface includes a portrait; entering a pose recommendation mode; and presenting a recommended human pose picture to a user in a predetermined preview manner, where the human pose picture is at least one picture that is selected from a picture library through metric learning and that has a top-ranked similarity to the to-be-taken image, and where the similarity is an overall similarity obtained by fusing a background similarity and a foreground similarity.

An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.

Image processing method and image processing apparatus

This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

Image segmentation method and image processing apparatus

Methods and systems for gesture-based control of a device are described. A virtual gesture-space is determined in a received input frame. The virtual gesture-space is associated with a primary user from a ranked user list of users. The received input frame is processed in only the virtual gesture-space, to detect and track a hand. Using a hand bounding box generated by detecting and tracking the hand, gesture classification is performed to determine a gesture input associated with the hand. A command input associated with the determined gesture input is processed. The device may be a smart television, a smart phone, a tablet, etc.

An image processing method includes detecting human skeleton key points of a character in an image, and for the to-be-processed image, generating a cropping region based on the human skeleton key points and a preset rule, and using the to-be-processed image selected by using the cropping region as an output object. The human skeleton key points and the preset rule are set based on photographing experience.

Image processing method and apparatus

In a method for selecting pictures from a sequence of pictures of an object in motion, a computerized user device determines, for each picture in the sequence of pictures, a value of a motion feature of the object. Based on analyzing the values of the motion feature of the pictures in the sequence, the device identifies a first subset of pictures from the pictures in the sequence. The device then selects, based on a second selection criterion, a second subset of pictures from the first subset of pictures. The pictures in the second subset are displayed to a user for further selection.

Picture selection method and related device

This application provides a gesture recognition method, and relates to the field of man-machine interaction technologies. The method includes: extracting M images from a first video segment in a video stream; performing gesture recognition on the M images by using a deep learning algorithm, to obtain a gesture recognition result corresponding to the first video segment; and performing result combination on gesture recognition results of N consecutive video segments including the first video segment, to obtain a combined gesture recognition result. In the foregoing recognition process, a gesture in the video stream does not need to be segmented or tracked, but phase actions are recognized by using a deep learning algorithm with a relatively fast calculation speed, and then the phase actions are combined, so as to improve a gesture recognition speed, and reduce a gesture recognition delay.

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Shenzhen, China

Songcen Xu