Beijing Baidu Netcom Science Technology Co., Ltd

Other

Boe Technology Group Co., Ltd.

Beijing Boe Display Technology Co., Ltd.

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Research Institute of Uranium Geology

Feng He

Qi Wang

Zhifan Feng

Chunguang Chai

Hu Yang

Yong Zhu

Shuai Chen

Bo Peng

Qiang Guo

Shan Jiang

Yunlong Zhang

PengFei Zhu

Mingkuan Qin

Xianglong Li

Xinyang Liu

Hu Liu

Ziying Li

Zilong Zhang

Jun Zhong

Ye Sun

Yuqi Cai

Xiaoqian Xiu

Xide Li

Chiheng Liu

Weitao Li

Yuyan Zhang

Qubo Wu

Licheng Jia

Wusheng Liu

Ruixiang Hao

Hongwei Ji

Sheng He

Zhixin Huang

Gui Wang

Xiaoran Duan

Jielin Zhang

Meizhi Han

Changkai Zhao

Jianfang Cai

Qingyin Guo

Quanyue Li

Shengfu Li

Zhongbo He

Junxian Wang

Linfei Qiu

Jinrong Lin

Wenquan Wang

Dai Qiang Yang

Xiaoneng Luo

Mingming Tian

Ke Wei Li

Changfa Yu

Longsheng Yi

Guo Wang

Jian Guo

The present disclosure relates to a method for analyzing a geological body with the help of physical and chemical properties thereof, and in particular, to a method for identifying an exudative sandstone uranium deposit. With the method for identifying an exudative sandstone uranium deposit according to the embodiments of the present disclosure, an exudative sandstone uranium deposit formed by an exudation metallogenesis may be systematically identified, so as to guide prediction and prospecting evaluation of a uranium deposit in red variegated sandstone formations of a sedimentary basin, avoid ore dislocation and ore leakage, open up new prospecting positions and spaces, and break through new uranium resources.

Method for identifying exudative sandstone uranium deposit

A computer-implemented method is provided. The method includes: obtaining a sample text and a sample image corresponding to the sample text; labeling a true semantic tag for the sample text according to a first preset rule; obtaining a text feature representation of the sample text and a predicted semantic tag output by a text coding sub-model; obtaining an image feature representation of the sample image output by an image coding sub-model; calculating a first loss based on the true semantic tag and the predicted semantic tag; calculating a contrast loss based on the text feature representation of the sample text and the image feature representation of the sample image; adjusting parameters of the text coding sub-model based on the first loss and the contrast loss; and adjusting parameters of the image coding sub-model based on the contrast loss.

Method for training image-text matching model, computing device, and storage medium

Disclosed are a method for processing multimodal data using a neural network, a device, and a medium, and relates to the field of artificial intelligence and, in particular to multimodal data processing, video classification, and deep learning. The neural network includes: an input subnetwork configured to receive the multimodal data to output respective first features of a plurality of modalities; a plurality of cross-modal feature subnetworks, each of which is configured to receive respective first features of two corresponding modalities to output a cross-modal feature corresponding to the two modalities; a plurality of cross-modal fusion subnetworks, each of which is configured to receive at least one cross-modal feature corresponding to a corresponding target modality and other modalities to output a second feature of the target modality; and an output subnetwork configured to receive respective second features of the plurality of modalities to output a processing result of the multimodal data.

Multimodal data processing

The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video.

Video classification method, electronic device and storage medium

Technical solutions for video event recognition relate to the fields of knowledge graphs, deep learning and computer vision. A video event graph is constructed, and each event in the video event graph includes: M argument roles of the event and respective arguments of the argument roles, with M being a positive integer greater than one. For a to-be-recognized video, respective arguments of the M argument roles of a to-be-recognized event corresponding to the video are acquired. According to the arguments acquired, an event is selected from the video event graph as a recognized event corresponding to the video.

Video event recognition method, electronic device and storage medium

The present disclosure provides an apparatus and method for measuring a clearance. The apparatus comprises: a first measurement component configured to be mounted on a first object in a mounted state thereof, so that a first measurement surface of the first measurement component is aligned with a first surface to be measured of the first object; and a second measurement component configured to be mounted on a second object in a mounted state thereof, so that a second measurement surface of the second measurement component is aligned with a second surface to be measured of the second object, wherein the first measurement component comprises a measurement instrument configured to measure a first distance between the measurement instrument and the first measurement surface and a second distance between the measurement instrument and the second measurement surface, wherein the clearance is calculated based on the first distance and the second distance.

Apparatuses and methods for measuring a clearance

A pneumatic card transport system is disclosed. The system is configured to use pressurized air to transport a card inserted to a conveying duct inside a card passageway assembly from a first end to a second end. Air nozzles are configured to release the pressurized airstreams at an angle relative to the conveying duct so that the card is essentially floating when traveling along the conveying duct. Hence, the card makes zero or minimum contact with the inner surface of the conveying duct. This will not only reduce the wear and tear of the card, but also reduces the traveling time inside the conveying duct.

Pneumatic card transport system

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Beijing, China

Feng He