Huawei Technologies Co., Limited

Huawei Cloud Computing Technologies Co., Ltd.

Dandan Tu

Jun Li

Xin Jin

Yong Zhang

Jing Wang

Changzheng Zhang

Jiahao Li

Jiajin Zhang

Yulei Xiao

Shiyue Zhuang

Xiaolong Bai

Youhua Zhang

Bian Yin

Hui Liang

Xiudong Wang

Yaoxin Li

Xiaoshi Chen

Pengpeng Zheng

Yibin Ye

Shenggao Zhu

Qi Du

A target region is first extracted from a to-be-recognized image, and then a defect coarse-selection region including an industrial defect is obtained from the target region. Through two times of region extraction, a proportion of a size of the industrial defect to a size of a background is increased, so that a probability of detecting a small-sized industrial defect can be increased. After the defect coarse-selection region including the industrial defect is detected, localization and size estimation are further performed on the industrial defect in the defect coarse-selection region.

Industrial defect recognition method and system, computing device, and storage medium

Embodiments of the present invention disclose a data processing apparatus. The apparatus is configured to: after calculating a set of gradient information of each parameter by using a sample data subset, delete the sample data subset, read a next sample data subset, calculate another set of gradient information of each parameter by using the next sample data subset, and accumulate a plurality of sets of calculated gradient information of each parameter, to obtain an update gradient of each parameter.

Data processing system and method

This application relates to the artificial intelligence field, and provides an image processing method, an apparatus, and a system. The image processing method includes: obtaining a plurality of image blocks by segmenting a to-be-analyzed pathological image; inputting the plurality of image blocks to a first analysis model to obtain a first analysis result, where the first analysis model classifies each of the plurality of image blocks based on a quantity or an area of suspicious lesion components; inputting at least one second-type image block in the first analysis result to a second analysis model to obtain a second analysis result, where the second analysis model analyzes a location of a suspicious lesion component of each input second-type image block; and obtaining a final analysis result of the pathological image based on the first analysis result and the second analysis result.

Image processing method, apparatus, and system

An image analysis method, including: obtaining influencing factors of t frames of images, where the influencing factors include self-owned features of h target subjects in each of the t frames of images and relational vector features between the h target subjects in each of the t frames of images, self-owned features of each target subject include a location feature, an attribute feature, and a posture feature, and t and h are natural numbers greater than 1; and obtaining a panoramic semantic description based on the influencing factors, where the panoramic semantic description includes a description of relationships between target subjects, relationships between actions of the target subjects and the target subjects, and relationships between the actions of the target subjects.

Image analysis method and system

An object detection model training method performed by a computing device, includes obtaining a system parameter including at least one of a receptive field of a backbone network, a size of a training image, a size of a to-be-detected object in the training image, a training computing capability, or a complexity of the to-be-detected object, determining a configuration parameter based on the system parameter, establishing a variable convolution network based on the configuration parameter and a feature map of the backbone network, recognizing the to-be-detected object based on a feature of the variable convolution network, and training the backbone network and the variable convolution network, where a convolution core used by any variable convolution layer may be offset in any direction in a process of performing convolution.

Object detection model training method and apparatus, and device

A feature set determining method includes obtaining, according to a received feature set determining request, data used for feature learning. The feature set determining request includes a learning objective of the feature learning. The method includes performing type analysis on the data to divide the data into first-type data and second-type data. The method includes performing semi-supervised learning on the first-type data to extract multiple first-type features. The method includes performing adaptive learning on the second-type data to extract multiple second-type features. The method includes evaluating the first-type features and the second-type features to obtain an optimal feature set.

Feature set determining method and apparatus

In an object detection model training method, a classifier that has been trained in a first phase is duplicated to at least two copies, and in a training in a second phase, each classifier obtained through duplication is configured to detect to-be-detected objects with different sizes, and train an object detection model based on a detection result.

Object detection model training method, apparatus, and device

An adaptive file storage method and apparatus is disclosed. The method includes determining a cold and hot attribute of a file, and performing coding storage processing or transcoding storage processing on the file according to the cold and hot attribute of the file. Therefore, a requirement of the cold and hot attribute of the file for storage overheads and restoration costs can be fully considered. In addition, the used coding technology has high reliability and a high coding speed. Therefore, comprehensive performance in multiple dimensions of storage overheads, restoration costs, reliability, and an coding speed can be improved.

Adaptive file storage method and apparatus

A method and a device for training a model in a distributed system are disclosed, so as to reduce load of a master node () during model training. The method includes: receiving, by a parameter server () in a first slave node (), a training result sent by a parameter client () in at least one slave node () in the distributed system, where the first slave node () is any slave node () in the distributed system, and a parameter client () in each slave node () obtains a training result by executing a training task corresponding to a sub-model stored on a parameter server () in the slave node (); and updating, by the parameter server () in the first slave node () based on the received training result, a sub-model stored on the parameter server in the first slave node.

Method and device for training model in distributed system

A method and an apparatus for reconstructing a cube in a multidimensional online analytical processing (MOLAP) system, where a cube is reconstructed based on a received reconstruction request and data stored in an old cube, and there is no need to acquire, from a database, data required for updating the cube, thereby ensuring data integrity when model reconstruction and data reconstruction are performed in the MOLAP system.

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Shenzhen, China

Dandan Tu