Beijing Baidu Netcom Science Technology Co., Ltd

Microsoft Technology Licensing, LLC

Ran Bi

Dianhai Yu

Yanjun Ma

Xiaoguang Hu

Yuning Du

Qiwen Liu

Qiao Zhao

Shengyu Wei

Wei Yang

Ying Zhou

Ruoyu Guo

Shuilong Dong

Xueying Lyu

Yi Liu

Cheng Cui

Weiwei Liu

Tingquan Gao

Xiaoting Yin

Sensen He

Shao Zeng

The present disclosure provides methods and apparatuses for multi-screen management. In some implementations, a plurality of connection requests may be received from a plurality of screen devices respectively. A screen host unit corresponding to each screen device may be created. A connection of each screen host unit to a corresponding screen device may be established. Screen display data may be transmitted, by at least one screen host unit, to at least one corresponding screen device through at least one corresponding connection respectively. Moreover, in some implementations, a connection request may be transmitted to a screen management hub. A connection to a corresponding screen host unit created by the screen management hub may be established. Screen display data may be received from the screen host unit through the connection. The screen display data may be displayed.

Multi-screen management

The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Method for training feature extraction model, method for classifying image, and related apparatuses

Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Image processing method and apparatus, device, medium and program product

There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Method and apparatus of training model, device, medium, and program product

The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Image recognition method and apparatus, electronic device and readable storage medium

Growing community of inventors

Beijing, China

Ran Bi