Tencent Technology (shenzhen) Company Limited

China Construction Steel Structure Corp. Ltd.

Tencent Technology Company Limited

Pai Peng

Xiaowei Guo

Kailin Wu

Yi Li

Xinyang Jiang

Huangbing Lv

Chengwei Wei

Jianchuan Huang

Qiushi Xia

This application discloses an image feature recognition method performed at a computing device. The computing device obtains a first neural network model by training parameters in a second neural network model using a first training set and a second training set consecutively, image features of training pictures in the first training set being marked and image features of training pictures in the second training set being not marked. After obtaining a first neural network model, the computing device obtains a recognition request for recognizing an image feature in a target picture. The computing device then recognizes the image feature in the target picture by applying the target picture to the first neural network model. Finally the computing device returns a first recognition result of the first neural network model, the first recognition result indicating an image feature (for example, a pathological feature) recognized in the target picture.

Image feature recognition method and apparatus, storage medium, and electronic apparatus

Embodiments of the present disclosure provide an image classification method for a computer device. The method includes obtaining an original image and a category of an object included in the original image; adjusting a display parameter of the original image to satisfy a value condition to obtain an adjusted original image; and transforming the display parameter of the original image according to a distribution condition that distribution of the display parameter needs to satisfy, to obtain a transformed image. The method also includes training a neural network model based on the category of the object and a training set constructed by the adjusted original image and the transformed image; and determining a category of an object included in a to-be-predicted image based on the trained neural network model.

Image classification method, computer device, and computer-readable storage medium

An image classification method includes: obtaining an original image and a category of an object included in the original image; adjusting a first display parameter of the original image to obtain an adjusted original image; and transforming a second display parameter of the original image to obtain a new image. The adjusted first display parameter satisfies a value condition; and the transformed second parameter of the new image satisfies a distribution condition. The method also includes training a neural network model based on the category of the included object and a training set constructed by combining the adjusted original image and the new image; and inputting a to-be-predicted image into the trained neural network model, and determining the category of the object included in the to-be-predicted.

Image classification method, server, and computer-readable storage medium

In some examples, processing circuitry obtains a shallow hash neural network (SHNN) model that has been trained from a HNN model and based on a set of SHNN training images that aggregates image recognition results from at least two reference hash neural network (HNN) models. Further, the processing circuitry performs an image recognition on the image according to the SHNN model, to obtain an image class vector in an image class space. The image class vector includes probability values of respective image classes in the image class space. A probability value of an image class in the image class space is a combination of intermediate probability values of the image class that are resulted from the at least two reference HNN models. Further, the processing circuitry determines one of the image classes for the image according to the probability values of the respective image classes in the image class space.

Image recognition method, apparatus, server, and storage medium

A method comprises: providing a weldment and a welded plate to which the weldment is to be welded, an end of the weldment to be welded to the welded plate being provided with a first welding face and a second welding face, the first welding face and the second welding face forming a pointed cone, the first welding face corresponding to a first welding area, and the second welding face corresponding to a second welding area; in the first welding area, welding being performed at the innermost side of the pointed cone to form a first welding pass; in the second welding area, welding being performed at the innermost side of the pointed cone to form a second welding pass, and then welding being performed at the outer side of the second welding pass to form a third welding pass adjacent to the second welding pass.

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Shenzhen, China

Pai Peng