Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Yongjian Wu

Feiyue Huang

Jilin Li

Chengjie Wang

Ke Li

Shouhong Ding

Yabiao Wang

Wei Li

Guofu Tan

Binghua Qian

Fuzhang Wu

Xingming Jin

Hui Ni

Yuan Huang

Yanhao Ge

Feng Gao

Yandan Zhao

Xiaoming Huang

Fangmai Zheng

Liqian Dong

Zhenye Gan

Bilei Zhu

Jing Liu

Jian Tong Li

Bin Zhang

Dong Chen

Yang Yang

Lu Zhang

Yuwing Tai

Qiang Fu

Shaoxin Li

Yunsheng Wu

Keke He

Ruixin Zhang

Zhibo Chen

Ling Zhao

Yicong Liang

Jinlong Peng

Xiaoqing Liang

Ke Mei

Da Yao

Fang Hou

Shang Wu

Yichao Yan

Baolong Yang

Junhong Huang

Boyuan Jiang

Mingyu Wu

Donghao Luo

Lizhao Gao

Fangmin Chen

Zhiwei Tao

Kejing Zhou

Hangyu Yan

Hengfeng Tian

Siqian Yang

Qingliang Lin

Changyou Deng

Boyuan Deng

Yichao Xiong

Yubin Xie

Yafeng Zhao

Xiaolong Liang

Liuji Zhou

Changwei He

Chenyang Guo

The present subject matter discloses an action recognition method, apparatus and device, a storage medium, and a computer program product, belonging to the field of image recognition. Multiple video frames in a target video are obtained. Feature extraction is performed on the multiple video frames respectively according to multiple dimensions to obtain multiple multi-channel feature patterns. Each video frame corresponds to one multi-channel feature pattern. Each channel represents one dimension. An attention weight of each multi-channel feature pattern is determined based on a similarity between every two multi-channel feature patterns. The attention weight is used for representing a degree of correlation between a corresponding multi-channel feature pattern and an action performed by an object in the target video. A type of the action is determined based on the multiple multi-channel feature patterns and the determined multiple attention weights.

Action recognition method, apparatus and device, storage medium and computer program product

A face detection method includes: acquiring a target image; invoking a face detection network, and processing the target image by using a feature extraction structure of the face detection network, to obtain original feature maps corresponding to the target image; the original feature maps having different resolutions; processing the original feature maps by using a feature enhancement structure of the face detection network, to obtain an enhanced feature map corresponding to each original feature map; the feature enhancement structure being obtained by searching a search space, and the search space used for searching the feature enhancement structure being determined based on a detection objective of the face detection network and a processing object of the feature enhancement structure; and processing the enhanced feature map by using a detection structure of the face detection network, to obtain a face detection result of the target image.

Face detection method, apparatus, and device, and training method, apparatus, and device for image detection neural network

An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.

Action recognition method and apparatus, computer storage medium, and computer device

This present disclosure describes a video image processing method and apparatus, a computer-readable medium and an electronic device, relating to the field of image processing technologies. The method includes determining, by a device, a target-object region in a current frame in a video. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes determining, by the device, a target-object tracking image in a next frame and corresponding to the target-object region; and sequentially performing, by the device, a plurality of sets of convolution processing on the target-object tracking image to determine a target-object region in the next frame. A quantity of convolutions of a first set of convolution processing in the plurality of sets of convolution processing is less than a quantity of convolutions of any other set of convolution processing.

Method, apparatus, and storage medium for processing video image

This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.

Method for generating high-resolution picture, computer device, and storage medium

A melody information processing method is described. A piece of Musical Instrument Digital Interface (MIDI) data corresponding to a song is received, a song identifier of the song is obtained, first melody information is generated according to the MIDI data, and the first melody information is stored in association with the song identifier in a melody database. Moreover, a user unaccompanied-singing audio data set that is uploaded from a user terminal is received, second melody information corresponding to the song identifier is extracted according to the user unaccompanied-singing audio data set, and the second melody information is stored in association with the song identifier in the melody database.

Method, server, and storage medium for melody information processing

A statistical parameter modeling method is performed by a server. After obtaining model training data, the model training data including a text feature sequence and a corresponding original speech sample sequence, the server inputs an original vector matrix formed by matching a text feature sample point in the text feature sample sequence with a speech sample point in the original speech sample sequence into a statistical parameter model for training and then performs non-linear mapping calculation on the original vector matrix in a hidden layer, to output a corresponding prediction speech sample point. The server then obtains a model parameter of the statistical parameter model according to the prediction speech sample point and a corresponding original speech sample point by using a smallest difference principle, to obtain a corresponding target statistical parameter model.

Statistical parameter model establishing method, speech synthesis method, server and storage medium

This application discloses a human attribute recognition method performed at a computing device. The method includes: determining a human body region image in a surveillance image; inputting the human body region image into a multi-attribute convolutional neural network model, to obtain, for each of a plurality of human attributes in the human body region image, a probability that the human attribute corresponds to a respective predefined attribute value, the multi-attribute convolutional neural network model being obtained by performing multi-attribute recognition and training on a set of pre-obtained training images by using a multi-attribute convolutional neural network; determining, for each of the plurality of human attributes in the human body region image, the attribute value of the human attribute based on the corresponding probability; and displaying the attribute values of the plurality of human attributes next to the human body region image.

Human body attribute recognition method, apparatus, and device and medium

A trajectory tracking method is provided for a computer device. The method includes performing motion tracking on head images in a plurality of video frames, to obtain motion trajectories corresponding to the head images; acquiring face images corresponding to the head images in the video frames, to obtain face image sets corresponding to the head images; determining from the face image sets corresponding to the head images, at least two face image sets having same face images; and combining motion trajectories corresponding to the at least two face image sets having same face images, to obtain a final motion trajectory of trajectory tracking.

Trajectory tracking method and apparatus, computer device, and storage medium

An identity vector generation method is provided. The method includes obtaining to-be-processed speech data. Corresponding acoustic features are extracted from the to-be-processed speech data. A posterior probability that each of the acoustic features belongs to each Gaussian distribution component in a speaker background model is calculated to obtain a statistic. The statistic is mapped to a statistic space to obtain a reference statistic, the statistic space built according to a statistic corresponding to a speech sample exceeding a threshold speech duration. A corrected statistic is determined according to the calculated statistic and the reference statistic; and an identity vector is generated according to the corrected statistic.

Identity vector generation method, computer device, and computer-readable storage medium

A method and an apparatus for training a voiceprint recognition system are provided. The method includes obtaining a voice training data set comprising voice segments of users; determining identity vectors of all the voice segments; identifying identity vectors of voice segments of a same user in the determined identity vectors; placing the recognized identity vectors of the same user in the users into one of user categories; and determining an identity vector in the user category as a first identity vector. The method further includes normalizing the first identity vector by using a normalization matrix, a first value being a sum of similarity degrees between the first identity vector in the corresponding category and other identity vectors in the corresponding category; training the normalization matrix, and outputting a training value of the normalization matrix when the normalization matrix maximizes a sum of first values of all the user categories.

Method and apparatus for training voiceprint recognition system

A facial tracking method is provided. The method includes: obtaining, from a video stream, an image that currently needs to be processed as a current image frame; and obtaining coordinates of facial key points in a previous image frame and a confidence level corresponding to the previous image frame. The method also includes calculating coordinates of facial key points in the current image frame according to the coordinates of the facial key points in the previous image frame when the confidence level is higher than a preset threshold; and performing multi-face recognition on the current image frame according to the coordinates of the facial key points in the current image frame. The method also includes calculating a confidence level of the coordinates of the facial key points in the current image frame, and returning to process a next frame until recognition on all image frames is completed.

Facial tracking method and apparatus, and storage medium

The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.

Image recognition method and apparatus

The embodiments of the present disclosure disclose a method for constructing a speech decoding network in digital speech recognition. The method comprises acquiring training data obtained by digital speech recording, the training data comprising a plurality of speech segments, and each speech segment comprising a plurality of digital speeches; performing acoustic feature extraction on the training data to obtain a feature sequence corresponding to each speech segment; performing progressive training starting from a mono-phoneme acoustic model to obtain an acoustic model; acquiring a language model, and constructing a speech decoding network by the language model and the acoustic model obtained by training.

Constructing speech decoding network for numeric speech recognition

A voice data processing method and apparatus are provided. The method includes obtaining an I-Vector vector of each of voice samples, and determining a target seed sample in the voice samples. A first cosine distance is calculated between an I-Vector vector of the target seed sample and an I-Vector vector of a target remaining voice sample, where the target remaining voice sample is a voice sample other than the target seed sample in the voice samples. A target voice sample is filtered from the voice samples or the target remaining voice sample according to the first cosine distance, to obtain a target voice sample whose first cosine distance is greater than a first threshold.

Voice data processing method, apparatus and storage medium

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Shenzhen, China

Yongjian Wu