Tencent Technology (shenzhen) Company Limited

Huawei Technologies Co., Limited

Tencent Technology Company Limited

Wenhao Jiang

Yan Zhang

Lin Ma

Xiaolong Zhu

Hao Wang

Wenhan Luo

Peng Yang

Jing He

Songping Yao

Haozhi Huang

Weiwei Fan

Ming Chang

Nan Wang

Shiyin Kang

Deyi Tuo

Shuo Zhang

Hongli Wang

Yongsen Zheng

Guangzhi Li

Xiaowen Cao

Shaobin Lin

Jingwen Wang

Zixin Zou

Siqing Du

Chong Chen

Xinpeng Chen

Youyi Zhang

Zaizhen Chen

Pinyi Li

A method includes: a processor that obtains several lines of data in to-be-displayed display data to generate a data block; generates a synchronization flag corresponding to the data block; encapsulates the data block and the synchronization flag corresponding to the data block to obtain a data packet corresponding to the data block; and sends all data packets corresponding to the display data to the display system. The display system sequentially parses all the data packets sent by the processor to obtain a synchronization flag associated with each data packet, and determines a display location of each data block on a display panel based on the synchronization flag to display the display data.

Display synchronization method, electronic device, and readable storage medium

Embodiments of this application disclose an image description generation method performed at a computing device. The method includes: obtaining a target image; generating a first global feature vector and a first label vector set of the target image; generating a first multi-mode feature vector of the target image through a matching model, the matching model being a model obtained through training according to a training image and reference image description information of the training image; and applying the first multi-mode feature vector, the first global feature vector, and the first label vector set to a computing model, to obtain the target image description information, the computing model being a model obtained through training according to image description information of the training image and the reference image description information.

Image description generation method, model training method, device and storage medium

This application relates to the field of artificial intelligence technologies, and in particular, to a training method of an image-text matching model, a bi-directional search method, and a relevant apparatus. The training method includes extracting a global feature and a local feature of an image sample; extracting a global feature and a local feature of a text sample; training a matching model according to the extracted global feature and local feature of the image sample and the extracted global feature and local feature of the text sample, to determine model parameters of the matching model; and determining, by the matching model, according to a global feature and a local feature of an inputted image and a global feature and a local feature of an inputted text, a matching degree between the image and the text.

Training method of image-text matching model, bi-directional search method, and relevant apparatus

A method, apparatus, and storage medium for training a neural network model used for image processing are described. The method includes: obtaining a plurality of video frames; inputting the plurality of video frames through a neural network model so that the neural network model outputs intermediate images; obtaining optical flow information between an early video frame and a later video frame; modifying an intermediate image corresponding to the early video frame according to the optical flow information to obtain an expected-intermediate image; determining a time loss between an intermediate image corresponding to the later video frame and the expected-intermediate image; determining a feature loss between the intermediate images and a target feature image; and training the neural network model according to the time loss and the feature loss, and returning to obtaining a plurality of video frames continue training until the neural network model satisfies a training finishing condition.

Method and apparatus for training neural network model used for image processing, and storage medium

This application relates to an abstract description generating method, an abstract description generation model training method, a computer device, and a storage medium. The abstract description generating method includes: inputting a labeled training sample into an abstract description generation model; performing first-phase training on an encoding network and a decoding network of the abstract description generation model based on supervision of a first loss function; obtaining a backward-derived hidden state of a previous moment through backward derivation according to a hidden state of each moment outputted by the decoding network; obtaining a value of a second loss function according to the backward-derived hidden state of the previous moment and an actual hidden state of the previous moment outputted by the decoding network; and obtaining final model parameters of the abstract description generation model determined based on supervision of the second loss function to reach a preset threshold value.

Summary generation method, summary generation model training method, and computer device

A terminal for generating music may identify, based on execution of scenario recognition, scenarios for images previously received by the terminal. The terminal may generate respective description texts for the scenarios. The terminal may execute keyword-based rhyme matching based on the respective description texts. The terminal may generate respective rhyming lyrics corresponding to the images. The terminal may convert the respective rhyming lyrics corresponding to the images into a speech. The terminal may synthesize the speech with preset background music to obtain image music.

Method and apparatus for generating music

Embodiments of this application disclose an image description generation method performed at a computing device. The method includes: obtaining a target image; generating a first global feature vector and a first label vector set of the target image; applying the target image to a matching model and generating a first multi-mode feature vector of the target image through the matching model, the matching model being a model obtained through training according to a training image and reference image description information of the training image; and generating target image description information of the target image according to the first multi-mode feature vector, the first global feature vector, and the first label vector set.

An image recognition method, a terminal, and a storage medium are provided. The method includes: performing feature extraction on a to-be-recognized image by using an encoder, to obtain a feature vector and a first annotation vector set; performing initialization processing on the feature vector, to obtain first initial input data; and generating first guiding information based on the first annotation vector set by using a first guiding network model. The first guiding network model is configured to generate guiding information according to an annotation vector set of any image. The method also includes determining a descriptive statement of the image based on the first guiding information, the first annotation vector set, and the first initial input data by using a decoder.

Image recognition method, terminal, and storage medium

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Shenzhen, China

Wenhao Jiang