Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Peking University

Ge Ou

Qiong Wu

Chun Long Wang

Shuai Wang

Xiangye Wei

Jie Feng

Yiming Bai

Yongming Shi

Yadong Mu

Chuqian Zhong

Pengfei Zhang

Zhenzhong Zhang

Zhiliang Wang

Jijing Huang

Dawei Tang

Hongxiang Shen

Chao Ji

Nan Zhuang

Guoqiang Gong

Guiyu Tian

Boran Jiang

Shuqi Wei

The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Method, apparatus and storage medium for training natural language processing model

The present disclosure provides an image processing method, apparatus, device, and computer-readable storage medium. The method includes: obtaining an image dataset, the image dataset including an image and an accompanying text related to an unseen class in the image; and generating a probability and/or distribution of the unseen class using an unseen class obtaining model, the probability and/or distribution of the unseen class including a probability that each pixel in the image is from the unseen class, a probability that the unseen class is present in the image, and a regional probability after the image is subdivided into a plurality of regions.

Image processing method, apparatus, and computer product for image segmentation using unseen class obtaining model

An image processing method and device, an image detecting method and system, and a storage medium are provided. The image processing method comprises: acquiring a target image; counting pixels of the target image to obtain a count value; adding compensation data at a peripheral position of an edge pixel of the target image according to the count value, to obtain pixel data of the compensated target image.

Image processing method and device, image detecting method and system, storage medium

An electronic apparatus and a method for displaying an image on a display device are disclosed. The electronic apparatus includes a display device; an image acquisition device configured to acquire a surrounding image of the display device; and a processor configured to: determine a background image of the display device according to the surrounding image; acquire a target range and a target object in the background image; determine a target image according to the background image, the target range and the target object; and control the display device to display the target image, wherein the target image excludes the target object.

Electronic apparatus, and method for displaying image on display device

An image processing model generation method includes: inputting at least one training sample lesion image into an initial image processing model, the initial image processing model including a classification layer and a marking layer; calling the classification layer; calling the marking layer; obtaining a loss value of the at least one training sample lesion image in the initial image processing model; determining whether the loss value is within a preset range; if not, updating parameters of the initial image processing model, an image processing model with updated parameters being used as an initial image processing model in next training; and repeating above steps until the loss value is within the preset range.

Image processing model generation method, image processing method and device, and electronic device

The present disclosure provides a method and apparatus of labeling a target in an image, and a computer recording medium. The method includes: acquiring a first neural network, the first neural network includes a multi-layer convolutional neural network and a fully connected layer, wherein each layer of the multi-layer convolutional neural network includes a convolutional layer, an activation function layer and a down-sampling layer arranged successively; processing the image by using the multi-layer convolutional neural network of the first neural network acquired so as to obtain a target position mask for the image; and labeling the target in the image based on the target position mask.

Method and apparatus of labeling target in image, and computer recording medium

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Beijing, China

Ge Ou